<article>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#article10_03_17_2239252</id>
	<title>Science and the Shortcomings of Statistics</title>
	<author>samzenpus</author>
	<datestamp>1268836200000</datestamp>
	<htmltext>Kilrah\_il writes <i>"The linked article provides a short summary of <a href="http://www.sciencenews.org/view/feature/id/57091/title/Odds\_Are,\_Its\_Wrong">the problems scientists have with statistics</a>. As an intern, I see it many times: Doctors do lots of research but don't have a clue when it comes to statistics &mdash; and in the social science area, it's even worse. From the article: 'Even when performed correctly, statistical tests are widely misunderstood and frequently misinterpreted. As a result, countless conclusions in the scientific literature are erroneous, and tests of medical dangers or treatments are often contradictory and confusing.'"</i></htmltext>
<tokenext>Kilrah \ _il writes " The linked article provides a short summary of the problems scientists have with statistics .
As an intern , I see it many times : Doctors do lots of research but do n't have a clue when it comes to statistics    and in the social science area , it 's even worse .
From the article : 'Even when performed correctly , statistical tests are widely misunderstood and frequently misinterpreted .
As a result , countless conclusions in the scientific literature are erroneous , and tests of medical dangers or treatments are often contradictory and confusing .
' "</tokentext>
<sentencetext>Kilrah\_il writes "The linked article provides a short summary of the problems scientists have with statistics.
As an intern, I see it many times: Doctors do lots of research but don't have a clue when it comes to statistics — and in the social science area, it's even worse.
From the article: 'Even when performed correctly, statistical tests are widely misunderstood and frequently misinterpreted.
As a result, countless conclusions in the scientific literature are erroneous, and tests of medical dangers or treatments are often contradictory and confusing.
'"</sentencetext>
</article>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519074</id>
	<title>Re:Personal experience</title>
	<author>WeirdJohn</author>
	<datestamp>1268846520000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>2</modscore>
	<htmltext><p>It's the approach that you can just pump the numbers into SPSS or Statistica, and then call on a battery of tests until you get a "significant" result that results in the kind of errors the article (and a disturbing number of<nobr> <wbr></nobr>/. readers) fall into.</p><p>Unless you're dealing with large samples, all z and t tests assume normality in the population, with insignificant skew or kurtosis.  Yet by definition, if we have enough data to be sure we have a normal population, we have enough data that the central limit theorem makes the differences moot.  Even more extreme, if we have a complete description of the population (a census) we have no need to use any inferential statistics.</p><p>Meanwhile students are told to test the data for normality, homoscedacticity and linearity, to the point where the repeated tests on a single data set make the chance of a Type II error better than even.  But by saying "SPSS said so" and burying assumptions beneath a mountain of waffle and misunderstood jargon we can still get these "results" published.</p><p>No-one who can't perform a balanced block design ANOVA by hand, or explain what transforming data does to residuals under assumptions of a linear additive model, should be allowed near statistical software in my opinion.  And the so-called statistics packages in popular spreadsheets should be banned, and any student relying on them should be failed.</p></htmltext>
<tokenext>It 's the approach that you can just pump the numbers into SPSS or Statistica , and then call on a battery of tests until you get a " significant " result that results in the kind of errors the article ( and a disturbing number of / .
readers ) fall into.Unless you 're dealing with large samples , all z and t tests assume normality in the population , with insignificant skew or kurtosis .
Yet by definition , if we have enough data to be sure we have a normal population , we have enough data that the central limit theorem makes the differences moot .
Even more extreme , if we have a complete description of the population ( a census ) we have no need to use any inferential statistics.Meanwhile students are told to test the data for normality , homoscedacticity and linearity , to the point where the repeated tests on a single data set make the chance of a Type II error better than even .
But by saying " SPSS said so " and burying assumptions beneath a mountain of waffle and misunderstood jargon we can still get these " results " published.No-one who ca n't perform a balanced block design ANOVA by hand , or explain what transforming data does to residuals under assumptions of a linear additive model , should be allowed near statistical software in my opinion .
And the so-called statistics packages in popular spreadsheets should be banned , and any student relying on them should be failed .</tokentext>
<sentencetext>It's the approach that you can just pump the numbers into SPSS or Statistica, and then call on a battery of tests until you get a "significant" result that results in the kind of errors the article (and a disturbing number of /.
readers) fall into.Unless you're dealing with large samples, all z and t tests assume normality in the population, with insignificant skew or kurtosis.
Yet by definition, if we have enough data to be sure we have a normal population, we have enough data that the central limit theorem makes the differences moot.
Even more extreme, if we have a complete description of the population (a census) we have no need to use any inferential statistics.Meanwhile students are told to test the data for normality, homoscedacticity and linearity, to the point where the repeated tests on a single data set make the chance of a Type II error better than even.
But by saying "SPSS said so" and burying assumptions beneath a mountain of waffle and misunderstood jargon we can still get these "results" published.No-one who can't perform a balanced block design ANOVA by hand, or explain what transforming data does to residuals under assumptions of a linear additive model, should be allowed near statistical software in my opinion.
And the so-called statistics packages in popular spreadsheets should be banned, and any student relying on them should be failed.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518484</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518352</id>
	<title>Attention White People!!</title>
	<author>Anonymous</author>
	<datestamp>1268839920000</datestamp>
	<modclass>None</modclass>
	<modscore>-1</modscore>
	<htmltext><p>I implore all my fellow race-aware white brethen to stop using Windoze.  Join the land of Linux where we have nigger-free code.  You don't want your computer, wife, girlfriend or daughters to be tainted by M$ code that isn't nigger-free do you?</p></htmltext>
<tokenext>I implore all my fellow race-aware white brethen to stop using Windoze .
Join the land of Linux where we have nigger-free code .
You do n't want your computer , wife , girlfriend or daughters to be tainted by M $ code that is n't nigger-free do you ?</tokentext>
<sentencetext>I implore all my fellow race-aware white brethen to stop using Windoze.
Join the land of Linux where we have nigger-free code.
You don't want your computer, wife, girlfriend or daughters to be tainted by M$ code that isn't nigger-free do you?</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521582</id>
	<title>Re:Lies, Damned Lies, and Statistics.</title>
	<author>schon</author>
	<datestamp>1268921100000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Don't forget that before the 20th century, most people only lived to 32!</p></htmltext>
<tokenext>Do n't forget that before the 20th century , most people only lived to 32 !</tokentext>
<sentencetext>Don't forget that before the 20th century, most people only lived to 32!</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518776</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521750</id>
	<title>Re:Summery?</title>
	<author>Anonymous</author>
	<datestamp>1268922000000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>The winter has been quite harsh and snowy so Summery is welcomed.</p></htmltext>
<tokenext>The winter has been quite harsh and snowy so Summery is welcomed .</tokentext>
<sentencetext>The winter has been quite harsh and snowy so Summery is welcomed.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518394</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519954</id>
	<title>Re:Example: Standard Deviation</title>
	<author>cycoj</author>
	<datestamp>1268903220000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>2</modscore>
	<htmltext><p>Have you ever looked at the sheer amount of knowledge that doctors have to know (and actually do know)? Yes they are learning baby physics and baby chemistry. We have physicists and chemists to do the non-baby physics and chemistry. You could also say the same thing for other sciences. I know friends who've taught physics to chemistry students and that was baby-physics which the chemists struggled to understand. Similarly I've had to learn chemistry for my physics degree and have pretty much forgotten almost anything about it, that didn't prevent me from getting a PhD in physics.<br>I wonder how much of the physics or chemistry out of your field of expertise you still remember.</p><p>Back to the topic of doctors, a lot of the stuff that doctors do is purely knowing things, but they need to do a lot of it. They don't necessarily know exactly how a drug works, they just know when to give that drug. So a lot of their work could be done with a very big flowchart, except for the fact that quite a lot is actually observation not just what you tell them.</p></htmltext>
<tokenext>Have you ever looked at the sheer amount of knowledge that doctors have to know ( and actually do know ) ?
Yes they are learning baby physics and baby chemistry .
We have physicists and chemists to do the non-baby physics and chemistry .
You could also say the same thing for other sciences .
I know friends who 've taught physics to chemistry students and that was baby-physics which the chemists struggled to understand .
Similarly I 've had to learn chemistry for my physics degree and have pretty much forgotten almost anything about it , that did n't prevent me from getting a PhD in physics.I wonder how much of the physics or chemistry out of your field of expertise you still remember.Back to the topic of doctors , a lot of the stuff that doctors do is purely knowing things , but they need to do a lot of it .
They do n't necessarily know exactly how a drug works , they just know when to give that drug .
So a lot of their work could be done with a very big flowchart , except for the fact that quite a lot is actually observation not just what you tell them .</tokentext>
<sentencetext>Have you ever looked at the sheer amount of knowledge that doctors have to know (and actually do know)?
Yes they are learning baby physics and baby chemistry.
We have physicists and chemists to do the non-baby physics and chemistry.
You could also say the same thing for other sciences.
I know friends who've taught physics to chemistry students and that was baby-physics which the chemists struggled to understand.
Similarly I've had to learn chemistry for my physics degree and have pretty much forgotten almost anything about it, that didn't prevent me from getting a PhD in physics.I wonder how much of the physics or chemistry out of your field of expertise you still remember.Back to the topic of doctors, a lot of the stuff that doctors do is purely knowing things, but they need to do a lot of it.
They don't necessarily know exactly how a drug works, they just know when to give that drug.
So a lot of their work could be done with a very big flowchart, except for the fact that quite a lot is actually observation not just what you tell them.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518852</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519160</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268847540000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>According to Wikipedia, standard deviation is the square root of the variance of a statistical population (or probability distribution). Is that what you were looking for? Please let me know - I'm trying to figure out what kind of a response you were looking for from that doctor. I already knew that standard deviation indicates how likely a given trial is to fall within ~68\% of the mean...</p></htmltext>
<tokenext>According to Wikipedia , standard deviation is the square root of the variance of a statistical population ( or probability distribution ) .
Is that what you were looking for ?
Please let me know - I 'm trying to figure out what kind of a response you were looking for from that doctor .
I already knew that standard deviation indicates how likely a given trial is to fall within ~ 68 \ % of the mean.. .</tokentext>
<sentencetext>According to Wikipedia, standard deviation is the square root of the variance of a statistical population (or probability distribution).
Is that what you were looking for?
Please let me know - I'm trying to figure out what kind of a response you were looking for from that doctor.
I already knew that standard deviation indicates how likely a given trial is to fall within ~68\% of the mean...</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518402</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518812</id>
	<title>Re:Lies, Damned Lies, and Statistics.</title>
	<author>Anonymous</author>
	<datestamp>1268843760000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>The problem with viewing statistics in medicine is that statistics are also used by politicians.  And since politicians are frequently liars, there is a strong correlation that using medicine will make you a liar.  Therefore, it is highly likely that 75\% of politicians have used medicine solely to make themselves liars, with a margin of error of 2.5\%.</p></htmltext>
<tokenext>The problem with viewing statistics in medicine is that statistics are also used by politicians .
And since politicians are frequently liars , there is a strong correlation that using medicine will make you a liar .
Therefore , it is highly likely that 75 \ % of politicians have used medicine solely to make themselves liars , with a margin of error of 2.5 \ % .</tokentext>
<sentencetext>The problem with viewing statistics in medicine is that statistics are also used by politicians.
And since politicians are frequently liars, there is a strong correlation that using medicine will make you a liar.
Therefore, it is highly likely that 75\% of politicians have used medicine solely to make themselves liars, with a margin of error of 2.5\%.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518344</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520812</id>
	<title>People can come with statistics to prove anything</title>
	<author>Anonymous</author>
	<datestamp>1268914680000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>40\% of all people know that</p></htmltext>
<tokenext>40 \ % of all people know that</tokentext>
<sentencetext>40\% of all people know that</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521194</id>
	<title>Re:only in medicine</title>
	<author>Anonymous</author>
	<datestamp>1268918700000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>2</modscore>
	<htmltext><p>Give some examples.  I mean, real, specific examples of mathematical practices or mathematical theories that are invalid and why they are such.  Based on what you said, my suspicion is you are basing your claim on a smattering of slashdot comments and no understanding of any of the physics you are referring to.  Several points give you away:</p><p>1) You speak of physics but your two vague examples are (I'm guessing because your description is almost unrecognizable) renormalization theory, and string theory.  You, and many others besides, forget that the many of physics sub-disciplines are not directly unconcerned with the former, and almost no one outside of high energy physics is involved in the latter.  In other words, your examples leave out the bulk of physics being done.</p><p>2) Renormalization theory involves demonstrating that apparent divergences will exactly cancel. You do not just discard them.  There was a saying that was popular in the 50's when people were developing the mathematical foundations for it: "Just because it is infinite, does not mean it is zero!".  It was an extremely important milestone when Freeman Dyson showed in the early 50's that all such divergences - obeying certain, explicit criteria - occurring in quantum electrodynamics were renormalizable. In case you weren't paying attention, Dyson was a mathematician.  In the following decades a lot of work was done to explore the mathematical properties of renormalizable theories, contrary to your assertion.<br>
&nbsp; &nbsp; &nbsp; Now many theories are not - in the strict mathematical sense - renormalizable.  In these cases, cutting off divergences is physically meaningful(condensed matter physics, where matter is discrete at small length scales), or physicists actively and openly discuss and search for ways to formulate theories that possess no divergences or are strictly renormalizable.  One may also ask, what if the correct theory is *not* renormalizable?  In other words, what if our theory, while mathematically sound, is physically inaccurate (which is the opposite of the bizzare paradigm you suggest)?  This is something actively discussed (and even widely assumed) in the search for new physics, but if true, the effects are too small to be currently detectable.  In other words, we are back to discarding things because they are small, which is standard practice.</p><p>3) String theory - which again, is actually a very small part of physics - is actually almost entirely mathematical, which you concede.  The mathematics is fine; the question is what, if anything, does it actually mean?  Your criticism makes no sense here - are you suggesting by having math taking over the physics, the math becomes bad?</p><p>4) You put accurate in quotes, as if to suggest it was a dubious claim.  This is disingenuous - in fields where a physicist is liable to claim this, it is demonstrably true; theories are able to predict many constants (such as the magnetic moment of the electron) to experimental precision.  Many general, quantitative phenomena that are predicted as a result of the mathematics have been experimentally verified.  (BCS superconductivity, Bose-Einstein condensates, Bohm-Aharanov effect, Quantum hall effect, etc).</p><p>5) More generally physics has often been less then mathematically rigorous as new theories are developed and refined.  Calculus - the basis for Newtonian physics - was not put on firm mathematical footing until the 19th century. And even then the intuitive form of calculus that Newton and Leibniz were thinking of was not formally developed until the 1960's(nonstandard analysis).  Part of the maturation of physical theories is the introduction of mathematically rigorous foundations.</p><p>Seriously, make some specific claims rather than casting blanket aspersions.  What physical theories today lack rigorous mathematical underpinning that physicists ignore?</p></htmltext>
<tokenext>Give some examples .
I mean , real , specific examples of mathematical practices or mathematical theories that are invalid and why they are such .
Based on what you said , my suspicion is you are basing your claim on a smattering of slashdot comments and no understanding of any of the physics you are referring to .
Several points give you away : 1 ) You speak of physics but your two vague examples are ( I 'm guessing because your description is almost unrecognizable ) renormalization theory , and string theory .
You , and many others besides , forget that the many of physics sub-disciplines are not directly unconcerned with the former , and almost no one outside of high energy physics is involved in the latter .
In other words , your examples leave out the bulk of physics being done.2 ) Renormalization theory involves demonstrating that apparent divergences will exactly cancel .
You do not just discard them .
There was a saying that was popular in the 50 's when people were developing the mathematical foundations for it : " Just because it is infinite , does not mean it is zero ! " .
It was an extremely important milestone when Freeman Dyson showed in the early 50 's that all such divergences - obeying certain , explicit criteria - occurring in quantum electrodynamics were renormalizable .
In case you were n't paying attention , Dyson was a mathematician .
In the following decades a lot of work was done to explore the mathematical properties of renormalizable theories , contrary to your assertion .
      Now many theories are not - in the strict mathematical sense - renormalizable .
In these cases , cutting off divergences is physically meaningful ( condensed matter physics , where matter is discrete at small length scales ) , or physicists actively and openly discuss and search for ways to formulate theories that possess no divergences or are strictly renormalizable .
One may also ask , what if the correct theory is * not * renormalizable ?
In other words , what if our theory , while mathematically sound , is physically inaccurate ( which is the opposite of the bizzare paradigm you suggest ) ?
This is something actively discussed ( and even widely assumed ) in the search for new physics , but if true , the effects are too small to be currently detectable .
In other words , we are back to discarding things because they are small , which is standard practice.3 ) String theory - which again , is actually a very small part of physics - is actually almost entirely mathematical , which you concede .
The mathematics is fine ; the question is what , if anything , does it actually mean ?
Your criticism makes no sense here - are you suggesting by having math taking over the physics , the math becomes bad ? 4 ) You put accurate in quotes , as if to suggest it was a dubious claim .
This is disingenuous - in fields where a physicist is liable to claim this , it is demonstrably true ; theories are able to predict many constants ( such as the magnetic moment of the electron ) to experimental precision .
Many general , quantitative phenomena that are predicted as a result of the mathematics have been experimentally verified .
( BCS superconductivity , Bose-Einstein condensates , Bohm-Aharanov effect , Quantum hall effect , etc ) .5 ) More generally physics has often been less then mathematically rigorous as new theories are developed and refined .
Calculus - the basis for Newtonian physics - was not put on firm mathematical footing until the 19th century .
And even then the intuitive form of calculus that Newton and Leibniz were thinking of was not formally developed until the 1960 's ( nonstandard analysis ) .
Part of the maturation of physical theories is the introduction of mathematically rigorous foundations.Seriously , make some specific claims rather than casting blanket aspersions .
What physical theories today lack rigorous mathematical underpinning that physicists ignore ?</tokentext>
<sentencetext>Give some examples.
I mean, real, specific examples of mathematical practices or mathematical theories that are invalid and why they are such.
Based on what you said, my suspicion is you are basing your claim on a smattering of slashdot comments and no understanding of any of the physics you are referring to.
Several points give you away:1) You speak of physics but your two vague examples are (I'm guessing because your description is almost unrecognizable) renormalization theory, and string theory.
You, and many others besides, forget that the many of physics sub-disciplines are not directly unconcerned with the former, and almost no one outside of high energy physics is involved in the latter.
In other words, your examples leave out the bulk of physics being done.2) Renormalization theory involves demonstrating that apparent divergences will exactly cancel.
You do not just discard them.
There was a saying that was popular in the 50's when people were developing the mathematical foundations for it: "Just because it is infinite, does not mean it is zero!".
It was an extremely important milestone when Freeman Dyson showed in the early 50's that all such divergences - obeying certain, explicit criteria - occurring in quantum electrodynamics were renormalizable.
In case you weren't paying attention, Dyson was a mathematician.
In the following decades a lot of work was done to explore the mathematical properties of renormalizable theories, contrary to your assertion.
      Now many theories are not - in the strict mathematical sense - renormalizable.
In these cases, cutting off divergences is physically meaningful(condensed matter physics, where matter is discrete at small length scales), or physicists actively and openly discuss and search for ways to formulate theories that possess no divergences or are strictly renormalizable.
One may also ask, what if the correct theory is *not* renormalizable?
In other words, what if our theory, while mathematically sound, is physically inaccurate (which is the opposite of the bizzare paradigm you suggest)?
This is something actively discussed (and even widely assumed) in the search for new physics, but if true, the effects are too small to be currently detectable.
In other words, we are back to discarding things because they are small, which is standard practice.3) String theory - which again, is actually a very small part of physics - is actually almost entirely mathematical, which you concede.
The mathematics is fine; the question is what, if anything, does it actually mean?
Your criticism makes no sense here - are you suggesting by having math taking over the physics, the math becomes bad?4) You put accurate in quotes, as if to suggest it was a dubious claim.
This is disingenuous - in fields where a physicist is liable to claim this, it is demonstrably true; theories are able to predict many constants (such as the magnetic moment of the electron) to experimental precision.
Many general, quantitative phenomena that are predicted as a result of the mathematics have been experimentally verified.
(BCS superconductivity, Bose-Einstein condensates, Bohm-Aharanov effect, Quantum hall effect, etc).5) More generally physics has often been less then mathematically rigorous as new theories are developed and refined.
Calculus - the basis for Newtonian physics - was not put on firm mathematical footing until the 19th century.
And even then the intuitive form of calculus that Newton and Leibniz were thinking of was not formally developed until the 1960's(nonstandard analysis).
Part of the maturation of physical theories is the introduction of mathematically rigorous foundations.Seriously, make some specific claims rather than casting blanket aspersions.
What physical theories today lack rigorous mathematical underpinning that physicists ignore?</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519820</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Opportunist</author>
	<datestamp>1268844180000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>4</modscore>
	<htmltext><p>Doctors are notoriously bad with statistics. But the real kings of bad statistics are psychiatrists.</p><p>Notice how a LOT of studies in psychiatry are essentially statistics, statistics and a bit of statistics? It might be the reason why a lot of the courses you have to pass to become a shrink also consist of a lot of statistics, statistics... you get the idea.</p><p>NOBODY who decides that his course of studies would be psychiatry decided for that because he enjoy statistics that much, though. Actually, most psych students struggle badly with statistics. Psychiatry is one of the fields where the label doesn't match the contents. It <i>looks</i> like you're going to do a lot of messing with people's minds (aka "solving their psychology problems") but actually, judging from the courses, you become a refined statistician who had a bit of a counceling tutoring on the side.</p><p>That's not what people become shrinks for, though. They want to sit in their office, put people on their couch (or, more modern, in a comfy chair) and get 100 bucks an hour for listening to some idiot whine. And most do just that and will do fine.</p><p>It gets bizarre when they somehow end up in a spot where they have to rely on their statistics. Hey, you got a masters in that, and that entails a buttload of statistics, so you can do it... Nobody really cares that 9 out of 10 that somehow managed to get their diploma by either learning what they absolutely needed (and forgot it right after the test, certain that they'd never need it again, because<nobr> <wbr></nobr>... ya know, listening to idiots and stuff, not sitting there plotting standard deviations...) or by cribbing altogether.</p><p>And then you get studies of the usefulness of psychotropic drugs and wonder whose black hole they pulled that out of...</p></htmltext>
<tokenext>Doctors are notoriously bad with statistics .
But the real kings of bad statistics are psychiatrists.Notice how a LOT of studies in psychiatry are essentially statistics , statistics and a bit of statistics ?
It might be the reason why a lot of the courses you have to pass to become a shrink also consist of a lot of statistics , statistics... you get the idea.NOBODY who decides that his course of studies would be psychiatry decided for that because he enjoy statistics that much , though .
Actually , most psych students struggle badly with statistics .
Psychiatry is one of the fields where the label does n't match the contents .
It looks like you 're going to do a lot of messing with people 's minds ( aka " solving their psychology problems " ) but actually , judging from the courses , you become a refined statistician who had a bit of a counceling tutoring on the side.That 's not what people become shrinks for , though .
They want to sit in their office , put people on their couch ( or , more modern , in a comfy chair ) and get 100 bucks an hour for listening to some idiot whine .
And most do just that and will do fine.It gets bizarre when they somehow end up in a spot where they have to rely on their statistics .
Hey , you got a masters in that , and that entails a buttload of statistics , so you can do it... Nobody really cares that 9 out of 10 that somehow managed to get their diploma by either learning what they absolutely needed ( and forgot it right after the test , certain that they 'd never need it again , because ... ya know , listening to idiots and stuff , not sitting there plotting standard deviations... ) or by cribbing altogether.And then you get studies of the usefulness of psychotropic drugs and wonder whose black hole they pulled that out of.. .</tokentext>
<sentencetext>Doctors are notoriously bad with statistics.
But the real kings of bad statistics are psychiatrists.Notice how a LOT of studies in psychiatry are essentially statistics, statistics and a bit of statistics?
It might be the reason why a lot of the courses you have to pass to become a shrink also consist of a lot of statistics, statistics... you get the idea.NOBODY who decides that his course of studies would be psychiatry decided for that because he enjoy statistics that much, though.
Actually, most psych students struggle badly with statistics.
Psychiatry is one of the fields where the label doesn't match the contents.
It looks like you're going to do a lot of messing with people's minds (aka "solving their psychology problems") but actually, judging from the courses, you become a refined statistician who had a bit of a counceling tutoring on the side.That's not what people become shrinks for, though.
They want to sit in their office, put people on their couch (or, more modern, in a comfy chair) and get 100 bucks an hour for listening to some idiot whine.
And most do just that and will do fine.It gets bizarre when they somehow end up in a spot where they have to rely on their statistics.
Hey, you got a masters in that, and that entails a buttload of statistics, so you can do it... Nobody really cares that 9 out of 10 that somehow managed to get their diploma by either learning what they absolutely needed (and forgot it right after the test, certain that they'd never need it again, because ... ya know, listening to idiots and stuff, not sitting there plotting standard deviations...) or by cribbing altogether.And then you get studies of the usefulness of psychotropic drugs and wonder whose black hole they pulled that out of...</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518402</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519114</id>
	<title>Re:Not Scientists</title>
	<author>Anonymous</author>
	<datestamp>1268846880000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p><div class="quote"><p>People in the real Sciences would have been forced to take enough Mathematics and/or Statistics to be able to properly interpret Statistics.</p></div><p>That's a real nice thought, but is simply not true.  I work for a big name pharma company doing Science, and I would estimate 70\% of us have PhDs (mostly Chemistry).  As a whole nobody is very confident in statistical analysis, as we have all taken the bare minimum of coursework in the subject.  If we need anything beyond a standard deviation, we call in for assistance.  I know it is nice to think that scientists are masters of several disciplines, but that is seldom true.  We are specialists, not generalists.</p></div>
	</htmltext>
<tokenext>People in the real Sciences would have been forced to take enough Mathematics and/or Statistics to be able to properly interpret Statistics.That 's a real nice thought , but is simply not true .
I work for a big name pharma company doing Science , and I would estimate 70 \ % of us have PhDs ( mostly Chemistry ) .
As a whole nobody is very confident in statistical analysis , as we have all taken the bare minimum of coursework in the subject .
If we need anything beyond a standard deviation , we call in for assistance .
I know it is nice to think that scientists are masters of several disciplines , but that is seldom true .
We are specialists , not generalists .</tokentext>
<sentencetext>People in the real Sciences would have been forced to take enough Mathematics and/or Statistics to be able to properly interpret Statistics.That's a real nice thought, but is simply not true.
I work for a big name pharma company doing Science, and I would estimate 70\% of us have PhDs (mostly Chemistry).
As a whole nobody is very confident in statistical analysis, as we have all taken the bare minimum of coursework in the subject.
If we need anything beyond a standard deviation, we call in for assistance.
I know it is nice to think that scientists are masters of several disciplines, but that is seldom true.
We are specialists, not generalists.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518750</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519022</id>
	<title>Re:Statistical assumptions are often ignored</title>
	<author>oldhack</author>
	<datestamp>1268845860000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>
I do remember this bit.  There is an assumption made of the underlying mechanics, represented as the distribution, and you're supposed to loop back to verify the assumption.
</p><p>
Stats is one subtle beast.</p></htmltext>
<tokenext>I do remember this bit .
There is an assumption made of the underlying mechanics , represented as the distribution , and you 're supposed to loop back to verify the assumption .
Stats is one subtle beast .</tokentext>
<sentencetext>
I do remember this bit.
There is an assumption made of the underlying mechanics, represented as the distribution, and you're supposed to loop back to verify the assumption.
Stats is one subtle beast.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518522</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519998</id>
	<title>Re:Not Scientists</title>
	<author>OrangeCatholic</author>
	<datestamp>1268903940000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>&gt;You seriously think this is a common problem in biomedical research?</p><p>Of course it is.  Medical studies are often condensed to a flashy headline in a newspaper.  "Scientists said X is true so it must be now."  Then the talking heads run off with it for the next three days.  Nobody - certainly not the journalist - reads the paper itself, and generally it's behind a paywall so there's virtually no point in ponying up the $30 to be the only person with an accurate assessment.</p><p>If anything, the media "spin" makes it drop-dead easy to have a medical paper say whatever you want, since nobody is going to check it.   Peer review?  Here's what happened when the FDA looked at the Vioxx (COX2) data.  It turns out the "peer review process" omitted the 12 and 15 month data points:</p><blockquote><div><p>However, when the Food and Drug Administration (FDA) later presented more complete data from the CLASS and VIGOR trials on its web site, the results were less certain. The CLASS trial was revealed to also have twelve and fifteen month time points which had not been discussed in the JAMA publication; in this segment of the trial, the number of ulcer-related complications for Celebrex caught up to the control NSAID group. <b>Similarly, the complete VIGOR study data revealed that in fact, when all adverse events, not just gastrointestinal, were tabulated, the patients receiving VIOXX had suffered (barely) significantly higher incidence of adverse events overall than the control NSAID group. In particular, the risk of serious cardiovascular thrombotic  events, e.g. myocardial infarction, was 1.7\% in the VIOXX patients versus 0.7\% in the control group, and there were significantly more withdrawals in the Vioxx group for causes including hypertension, edema, hepatotoxicity, heart failure, or pathological laboratory findings. The mean increases in systolic and diastolic blood pressure in the Vioxx group were 4.6 mmHg and 1.7 mmHg respectively, compared to 1.0 and 0.1 mmHg in the control NSAID group.</b> An estimated 43,000,000 Americans, nearly one out of six, suffers from arthritis. However, 42\% (18 million) of these also suffer from hypertension. Therefore, the promise of better patient outcomes and lowered medical costs from use of COX-2 inhibitors may not be as great as previously hoped.</p></div></blockquote></div>
	</htmltext>
<tokenext>&gt; You seriously think this is a common problem in biomedical research ? Of course it is .
Medical studies are often condensed to a flashy headline in a newspaper .
" Scientists said X is true so it must be now .
" Then the talking heads run off with it for the next three days .
Nobody - certainly not the journalist - reads the paper itself , and generally it 's behind a paywall so there 's virtually no point in ponying up the $ 30 to be the only person with an accurate assessment.If anything , the media " spin " makes it drop-dead easy to have a medical paper say whatever you want , since nobody is going to check it .
Peer review ?
Here 's what happened when the FDA looked at the Vioxx ( COX2 ) data .
It turns out the " peer review process " omitted the 12 and 15 month data points : However , when the Food and Drug Administration ( FDA ) later presented more complete data from the CLASS and VIGOR trials on its web site , the results were less certain .
The CLASS trial was revealed to also have twelve and fifteen month time points which had not been discussed in the JAMA publication ; in this segment of the trial , the number of ulcer-related complications for Celebrex caught up to the control NSAID group .
Similarly , the complete VIGOR study data revealed that in fact , when all adverse events , not just gastrointestinal , were tabulated , the patients receiving VIOXX had suffered ( barely ) significantly higher incidence of adverse events overall than the control NSAID group .
In particular , the risk of serious cardiovascular thrombotic events , e.g .
myocardial infarction , was 1.7 \ % in the VIOXX patients versus 0.7 \ % in the control group , and there were significantly more withdrawals in the Vioxx group for causes including hypertension , edema , hepatotoxicity , heart failure , or pathological laboratory findings .
The mean increases in systolic and diastolic blood pressure in the Vioxx group were 4.6 mmHg and 1.7 mmHg respectively , compared to 1.0 and 0.1 mmHg in the control NSAID group .
An estimated 43,000,000 Americans , nearly one out of six , suffers from arthritis .
However , 42 \ % ( 18 million ) of these also suffer from hypertension .
Therefore , the promise of better patient outcomes and lowered medical costs from use of COX-2 inhibitors may not be as great as previously hoped .</tokentext>
<sentencetext>&gt;You seriously think this is a common problem in biomedical research?Of course it is.
Medical studies are often condensed to a flashy headline in a newspaper.
"Scientists said X is true so it must be now.
"  Then the talking heads run off with it for the next three days.
Nobody - certainly not the journalist - reads the paper itself, and generally it's behind a paywall so there's virtually no point in ponying up the $30 to be the only person with an accurate assessment.If anything, the media "spin" makes it drop-dead easy to have a medical paper say whatever you want, since nobody is going to check it.
Peer review?
Here's what happened when the FDA looked at the Vioxx (COX2) data.
It turns out the "peer review process" omitted the 12 and 15 month data points:However, when the Food and Drug Administration (FDA) later presented more complete data from the CLASS and VIGOR trials on its web site, the results were less certain.
The CLASS trial was revealed to also have twelve and fifteen month time points which had not been discussed in the JAMA publication; in this segment of the trial, the number of ulcer-related complications for Celebrex caught up to the control NSAID group.
Similarly, the complete VIGOR study data revealed that in fact, when all adverse events, not just gastrointestinal, were tabulated, the patients receiving VIOXX had suffered (barely) significantly higher incidence of adverse events overall than the control NSAID group.
In particular, the risk of serious cardiovascular thrombotic  events, e.g.
myocardial infarction, was 1.7\% in the VIOXX patients versus 0.7\% in the control group, and there were significantly more withdrawals in the Vioxx group for causes including hypertension, edema, hepatotoxicity, heart failure, or pathological laboratory findings.
The mean increases in systolic and diastolic blood pressure in the Vioxx group were 4.6 mmHg and 1.7 mmHg respectively, compared to 1.0 and 0.1 mmHg in the control NSAID group.
An estimated 43,000,000 Americans, nearly one out of six, suffers from arthritis.
However, 42\% (18 million) of these also suffer from hypertension.
Therefore, the promise of better patient outcomes and lowered medical costs from use of COX-2 inhibitors may not be as great as previously hoped.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518958</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31522050</id>
	<title>not specific to statistics</title>
	<author>drfireman</author>
	<datestamp>1268923500000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Misuse of statistics is well-represented in scientific articles.  Other things that are well-represented are poor knowledge and reasoning in the area of the subject discipline, inept writing, misleading or unhelpful graphics, poor scholarship, etc.  Sturgeon's Law applies across the board.</p><p>Having read a fair number of sky-is-falling articles about statistics in science, and having worked with my share of researchers (MDs and PhDs in a variety of fields) who think everything is rosy, I'm pretty sure that the truth is somewhere in between.  A minor saving grace is the fact that getting the statistics wrong is not the same as getting the answer wrong.  Although it's certainly quite common to find published articles that make claims with no support whatsoever, in my experience it's much more common to find articles where the inappropriate statistics just mean the support isn't nearly as strong as claimed.  Spurious results tend, though not as reliably as we'd like, to get weeded out by the literature.  I rarely read an article that isn't specifically about methodology in which the methods/statistics are really solid, but I also rarely read an article in which unsound statistics undermines the entire contribution.</p></htmltext>
<tokenext>Misuse of statistics is well-represented in scientific articles .
Other things that are well-represented are poor knowledge and reasoning in the area of the subject discipline , inept writing , misleading or unhelpful graphics , poor scholarship , etc .
Sturgeon 's Law applies across the board.Having read a fair number of sky-is-falling articles about statistics in science , and having worked with my share of researchers ( MDs and PhDs in a variety of fields ) who think everything is rosy , I 'm pretty sure that the truth is somewhere in between .
A minor saving grace is the fact that getting the statistics wrong is not the same as getting the answer wrong .
Although it 's certainly quite common to find published articles that make claims with no support whatsoever , in my experience it 's much more common to find articles where the inappropriate statistics just mean the support is n't nearly as strong as claimed .
Spurious results tend , though not as reliably as we 'd like , to get weeded out by the literature .
I rarely read an article that is n't specifically about methodology in which the methods/statistics are really solid , but I also rarely read an article in which unsound statistics undermines the entire contribution .</tokentext>
<sentencetext>Misuse of statistics is well-represented in scientific articles.
Other things that are well-represented are poor knowledge and reasoning in the area of the subject discipline, inept writing, misleading or unhelpful graphics, poor scholarship, etc.
Sturgeon's Law applies across the board.Having read a fair number of sky-is-falling articles about statistics in science, and having worked with my share of researchers (MDs and PhDs in a variety of fields) who think everything is rosy, I'm pretty sure that the truth is somewhere in between.
A minor saving grace is the fact that getting the statistics wrong is not the same as getting the answer wrong.
Although it's certainly quite common to find published articles that make claims with no support whatsoever, in my experience it's much more common to find articles where the inappropriate statistics just mean the support isn't nearly as strong as claimed.
Spurious results tend, though not as reliably as we'd like, to get weeded out by the literature.
I rarely read an article that isn't specifically about methodology in which the methods/statistics are really solid, but I also rarely read an article in which unsound statistics undermines the entire contribution.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518674</id>
	<title>50\% of all statistics are useless</title>
	<author>davidwr</author>
	<datestamp>1268842680000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>+/- 50\%*.</p><p>*confidence interval=100\%</p></htmltext>
<tokenext>+ /- 50 \ % * .
* confidence interval = 100 \ %</tokentext>
<sentencetext>+/- 50\%*.
*confidence interval=100\%</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519032</id>
	<title>Re:Summery?</title>
	<author>icannotthinkofaname</author>
	<datestamp>1268845920000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>2</modscore>
	<htmltext><p>That would be <a href="http://en.wikipedia.org/wiki/Muphry's\_law" title="wikipedia.org">Muphry's law</a> [wikipedia.org].</p><p>For details on Muphry's law, click on the above hyperlink.  For more fun laws, click on the below hyperlink.</p><p><a href="http://en.wikipedia.org/wiki/List\_of\_eponymous\_laws" title="wikipedia.org">More fun here.</a> [wikipedia.org]</p></htmltext>
<tokenext>That would be Muphry 's law [ wikipedia.org ] .For details on Muphry 's law , click on the above hyperlink .
For more fun laws , click on the below hyperlink.More fun here .
[ wikipedia.org ]</tokentext>
<sentencetext>That would be Muphry's law [wikipedia.org].For details on Muphry's law, click on the above hyperlink.
For more fun laws, click on the below hyperlink.More fun here.
[wikipedia.org]</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518672</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518660</id>
	<title>stop making them vie for grant money</title>
	<author>Anonymous</author>
	<datestamp>1268842620000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext>maybe we'd get some honest science if it wasn't a bidding war.</htmltext>
<tokenext>maybe we 'd get some honest science if it was n't a bidding war .</tokentext>
<sentencetext>maybe we'd get some honest science if it wasn't a bidding war.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521970</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268923200000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Don't forget that those who want to help people usually don't do much research, and the other way round. They who actually want to do science usually like or have arranged with statistics. And, as it takes up so much part of psychology studies, they are probably better at it than average natural science graduates.<br>There are really to types of people pursuing that field of studies: Altruists and careerist. The latter not seldom aim for universitary merits, and for that, you need statistical knowledge. Probably.</p></htmltext>
<tokenext>Do n't forget that those who want to help people usually do n't do much research , and the other way round .
They who actually want to do science usually like or have arranged with statistics .
And , as it takes up so much part of psychology studies , they are probably better at it than average natural science graduates.There are really to types of people pursuing that field of studies : Altruists and careerist .
The latter not seldom aim for universitary merits , and for that , you need statistical knowledge .
Probably .</tokentext>
<sentencetext>Don't forget that those who want to help people usually don't do much research, and the other way round.
They who actually want to do science usually like or have arranged with statistics.
And, as it takes up so much part of psychology studies, they are probably better at it than average natural science graduates.There are really to types of people pursuing that field of studies: Altruists and careerist.
The latter not seldom aim for universitary merits, and for that, you need statistical knowledge.
Probably.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518348</id>
	<title>Its common knowledge</title>
	<author>Johnny Fusion</author>
	<datestamp>1268839860000</datestamp>
	<modclass>Redundant</modclass>
	<modscore>0</modscore>
	<htmltext>That 77.28\% of all statistics are made up.</htmltext>
<tokenext>That 77.28 \ % of all statistics are made up .</tokentext>
<sentencetext>That 77.28\% of all statistics are made up.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521380</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268919840000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>I guess that there is a bell shaped curve somwhere, which shows the variation of statistical knowledge.</p><p>It all just shows how thick society is, where people pay money to scientists just to prove a point they want made. All they have to do is work out how to fudge results effectively! For example, I want to prove that speed isn't a crime, so I look at all deaths due to travelling and find that supprise supprise, it's not speed after all. If it is, then I can always take a different sample.</p></htmltext>
<tokenext>I guess that there is a bell shaped curve somwhere , which shows the variation of statistical knowledge.It all just shows how thick society is , where people pay money to scientists just to prove a point they want made .
All they have to do is work out how to fudge results effectively !
For example , I want to prove that speed is n't a crime , so I look at all deaths due to travelling and find that supprise supprise , it 's not speed after all .
If it is , then I can always take a different sample .</tokentext>
<sentencetext>I guess that there is a bell shaped curve somwhere, which shows the variation of statistical knowledge.It all just shows how thick society is, where people pay money to scientists just to prove a point they want made.
All they have to do is work out how to fudge results effectively!
For example, I want to prove that speed isn't a crime, so I look at all deaths due to travelling and find that supprise supprise, it's not speed after all.
If it is, then I can always take a different sample.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518852</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518760</id>
	<title>Re:Its common knowledge</title>
	<author>Anonymous</author>
	<datestamp>1268843460000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>3</modscore>
	<htmltext><p>And 77.335\% of all statistics claim more accuracy than their expected deviation warrants.</p></htmltext>
<tokenext>And 77.335 \ % of all statistics claim more accuracy than their expected deviation warrants .</tokentext>
<sentencetext>And 77.335\% of all statistics claim more accuracy than their expected deviation warrants.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518348</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519530</id>
	<title>Re:Personal experience</title>
	<author>failedlogic</author>
	<datestamp>1268852940000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>2</modscore>
	<htmltext><p>I think the term "Statistics" has become too general that people don't understand how complicated it can be. People think of Statistics as Bob saying to Alice - "Get me the stats on this weeks' sales." Alice just goes digging around and gets Bob the total sales in $, # of units sold<nobr> <wbr></nobr>.... etc. People don't understand or know of the concepts that are involved in polling, they just thing they called 2,000 random people and that's it. That's statistics to the public and many college graduates.</p><p>I had to take a few stats courses for my BA. Learning stats is humbling - I know I really know nothing about it now after taking a few courses. The classes I took assumed you have no inkling of the basics of calculus or algebra (much past grade 9 level). I didn't know any calculus - I took some 1000 level Algebra but, after graduating a few years ago, I'm teaching my self Calc now and I'm realizing how much less I really understood about Stats at the time.</p><p>When people really don't understand the underlying mathematical principles they shouldn't use SPSS or Excel. Heck, if you ask people what 2+2 is, they know the answer. But you tell them to apply X or Y to such and such data set with SPSS, they probably won't investigate the results. Print it out with the report. Done! If you use a Stats program you should understand what your data means, what is happening to your data, what it means when X is applied to your data and what the end result means. I don't think a lot of people are humble enough to say they don't really understand. Little white lies!</p></htmltext>
<tokenext>I think the term " Statistics " has become too general that people do n't understand how complicated it can be .
People think of Statistics as Bob saying to Alice - " Get me the stats on this weeks ' sales .
" Alice just goes digging around and gets Bob the total sales in $ , # of units sold .... etc. People do n't understand or know of the concepts that are involved in polling , they just thing they called 2,000 random people and that 's it .
That 's statistics to the public and many college graduates.I had to take a few stats courses for my BA .
Learning stats is humbling - I know I really know nothing about it now after taking a few courses .
The classes I took assumed you have no inkling of the basics of calculus or algebra ( much past grade 9 level ) .
I did n't know any calculus - I took some 1000 level Algebra but , after graduating a few years ago , I 'm teaching my self Calc now and I 'm realizing how much less I really understood about Stats at the time.When people really do n't understand the underlying mathematical principles they should n't use SPSS or Excel .
Heck , if you ask people what 2 + 2 is , they know the answer .
But you tell them to apply X or Y to such and such data set with SPSS , they probably wo n't investigate the results .
Print it out with the report .
Done ! If you use a Stats program you should understand what your data means , what is happening to your data , what it means when X is applied to your data and what the end result means .
I do n't think a lot of people are humble enough to say they do n't really understand .
Little white lies !</tokentext>
<sentencetext>I think the term "Statistics" has become too general that people don't understand how complicated it can be.
People think of Statistics as Bob saying to Alice - "Get me the stats on this weeks' sales.
" Alice just goes digging around and gets Bob the total sales in $, # of units sold .... etc. People don't understand or know of the concepts that are involved in polling, they just thing they called 2,000 random people and that's it.
That's statistics to the public and many college graduates.I had to take a few stats courses for my BA.
Learning stats is humbling - I know I really know nothing about it now after taking a few courses.
The classes I took assumed you have no inkling of the basics of calculus or algebra (much past grade 9 level).
I didn't know any calculus - I took some 1000 level Algebra but, after graduating a few years ago, I'm teaching my self Calc now and I'm realizing how much less I really understood about Stats at the time.When people really don't understand the underlying mathematical principles they shouldn't use SPSS or Excel.
Heck, if you ask people what 2+2 is, they know the answer.
But you tell them to apply X or Y to such and such data set with SPSS, they probably won't investigate the results.
Print it out with the report.
Done! If you use a Stats program you should understand what your data means, what is happening to your data, what it means when X is applied to your data and what the end result means.
I don't think a lot of people are humble enough to say they don't really understand.
Little white lies!</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518484</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519280</id>
	<title>Re:Example: Standard Deviation</title>
	<author>rve</author>
	<datestamp>1268849100000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>3</modscore>
	<htmltext><p>You're mixing up psychiatrists, psychologists and psychotherapists.<br>A psychiatrist went to med school, got a doctors degree and specialized in problems with the brain. A psychologist went to university to learn the study of behavior of people. This involves a lot of statistics and many of them probably do consider it something they didn't go to college for, but it's a study that is supposed to follow the scientific method and prepare students for doing research, not therapy.</p><p>A psychotherapist is anyone who feels like calling themselves that. As a preparation they may have studied psychology at university, or they may have spent 20 years meditating in the Himalayas, or followed a short course at a religious group such as an institute of multiple personality disorder therapists or scientology.</p></htmltext>
<tokenext>You 're mixing up psychiatrists , psychologists and psychotherapists.A psychiatrist went to med school , got a doctors degree and specialized in problems with the brain .
A psychologist went to university to learn the study of behavior of people .
This involves a lot of statistics and many of them probably do consider it something they did n't go to college for , but it 's a study that is supposed to follow the scientific method and prepare students for doing research , not therapy.A psychotherapist is anyone who feels like calling themselves that .
As a preparation they may have studied psychology at university , or they may have spent 20 years meditating in the Himalayas , or followed a short course at a religious group such as an institute of multiple personality disorder therapists or scientology .</tokentext>
<sentencetext>You're mixing up psychiatrists, psychologists and psychotherapists.A psychiatrist went to med school, got a doctors degree and specialized in problems with the brain.
A psychologist went to university to learn the study of behavior of people.
This involves a lot of statistics and many of them probably do consider it something they didn't go to college for, but it's a study that is supposed to follow the scientific method and prepare students for doing research, not therapy.A psychotherapist is anyone who feels like calling themselves that.
As a preparation they may have studied psychology at university, or they may have spent 20 years meditating in the Himalayas, or followed a short course at a religious group such as an institute of multiple personality disorder therapists or scientology.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520444</id>
	<title>Re:The problem is statisticians</title>
	<author>Hatman39</author>
	<datestamp>1268910600000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Being a PhD student who mostly works with statistics (even though I have little formal training in it), I can attest to the truth of this. Sometimes you get results that do not vibe with what you are seeing, or that are doubtfull in some other way. Of course, we can blindly listen to the stats, or we can find out why the results are as they are. I try to do the latter, but the 'SPSS effect' tends to promote the former.</htmltext>
<tokenext>Being a PhD student who mostly works with statistics ( even though I have little formal training in it ) , I can attest to the truth of this .
Sometimes you get results that do not vibe with what you are seeing , or that are doubtfull in some other way .
Of course , we can blindly listen to the stats , or we can find out why the results are as they are .
I try to do the latter , but the 'SPSS effect ' tends to promote the former .</tokentext>
<sentencetext>Being a PhD student who mostly works with statistics (even though I have little formal training in it), I can attest to the truth of this.
Sometimes you get results that do not vibe with what you are seeing, or that are doubtfull in some other way.
Of course, we can blindly listen to the stats, or we can find out why the results are as they are.
I try to do the latter, but the 'SPSS effect' tends to promote the former.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519224</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518482</id>
	<title>Re:Long winded troll</title>
	<author>khallow</author>
	<datestamp>1268841180000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>The entire article can be summed up by the tiresome cliche "correlation != causation".</p> </div><p>That misses a lot of the problem. For example, observer bias through poor statistical design of the experiment or throwing out data can cause the appearance of correlation or causation in data that isn't so.</p></div>
	</htmltext>
<tokenext>The entire article can be summed up by the tiresome cliche " correlation ! = causation " .
That misses a lot of the problem .
For example , observer bias through poor statistical design of the experiment or throwing out data can cause the appearance of correlation or causation in data that is n't so .</tokentext>
<sentencetext>The entire article can be summed up by the tiresome cliche "correlation != causation".
That misses a lot of the problem.
For example, observer bias through poor statistical design of the experiment or throwing out data can cause the appearance of correlation or causation in data that isn't so.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518396</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521654</id>
	<title>Shortcomings of of the math? No...</title>
	<author>Mashdar</author>
	<datestamp>1268921460000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Shortcomings of statistics? More like shortcomings of humans *attempting* to use statistics.</htmltext>
<tokenext>Shortcomings of statistics ?
More like shortcomings of humans * attempting * to use statistics .</tokentext>
<sentencetext>Shortcomings of statistics?
More like shortcomings of humans *attempting* to use statistics.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520096</id>
	<title>Re:Example: Standard Deviation</title>
	<author>geoffhall</author>
	<datestamp>1268905260000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>My doctor was explaining to me that my blood sugar readings should not have a standard deviation of more than 1/3rd of the average blood sugar reading. Just to test if he knew what it meant, I asked him what a standard deviation was. Oh the fun when he tried to bullshit his way out of that one! He eventually told me that when I plot my data in Excel I can ask it to give me statistics on the column and it would mention what the standard deviation value was. But when I pressed on and asked him what a standard deviation is, he shooed me off and told me to go look it up. Never did he confess that he had no clue.</p></div><p>I think you a being silly. The core of your doctor-patient relationship should be you trust he has the knowledge and judgement to treat your condition in a professional and considered manner. If he does not understand the statistics, it should not be a concern as many primary practitioners are not specialists, especially arcane things like deriving standard deviation. Doctors are typically foremost clinicians, not chemists, mathematicians, grammar freaks, computer geeks etc... If you are not comfortable with your doctor, find another. Anyway, doctors should follow sound scientific principals in their treatment but should NOT treat on the basis of numbers alone. It is enough other trusted and respected specialists distil the relevant information and say "this is best practice in this situation". This is usually called CME (continuing medical education). Access to literature on the internet should allow you read up on whether the regimen you are on is reasonable or not. Be aware, you WILL find published articles FOR and AGAINST any kind of treatment in medical journals and the REAL skill is knowing the context and the clinical biases that can occur and whether a particular therapy is appropriate. Diseases or conditions rarely exist in total isolation or as a single entity. Doctors have enough on their daily plate without other concerns in other areas. We TRUST nurses will take accurate readings of temp, BP etc, we trust pharmacists will dispense correct dosage, we trust OT to sterilise equipment adequately and we trust EXCEL to do SD. So???</p></div>
	</htmltext>
<tokenext>My doctor was explaining to me that my blood sugar readings should not have a standard deviation of more than 1/3rd of the average blood sugar reading .
Just to test if he knew what it meant , I asked him what a standard deviation was .
Oh the fun when he tried to bullshit his way out of that one !
He eventually told me that when I plot my data in Excel I can ask it to give me statistics on the column and it would mention what the standard deviation value was .
But when I pressed on and asked him what a standard deviation is , he shooed me off and told me to go look it up .
Never did he confess that he had no clue.I think you a being silly .
The core of your doctor-patient relationship should be you trust he has the knowledge and judgement to treat your condition in a professional and considered manner .
If he does not understand the statistics , it should not be a concern as many primary practitioners are not specialists , especially arcane things like deriving standard deviation .
Doctors are typically foremost clinicians , not chemists , mathematicians , grammar freaks , computer geeks etc... If you are not comfortable with your doctor , find another .
Anyway , doctors should follow sound scientific principals in their treatment but should NOT treat on the basis of numbers alone .
It is enough other trusted and respected specialists distil the relevant information and say " this is best practice in this situation " .
This is usually called CME ( continuing medical education ) .
Access to literature on the internet should allow you read up on whether the regimen you are on is reasonable or not .
Be aware , you WILL find published articles FOR and AGAINST any kind of treatment in medical journals and the REAL skill is knowing the context and the clinical biases that can occur and whether a particular therapy is appropriate .
Diseases or conditions rarely exist in total isolation or as a single entity .
Doctors have enough on their daily plate without other concerns in other areas .
We TRUST nurses will take accurate readings of temp , BP etc , we trust pharmacists will dispense correct dosage , we trust OT to sterilise equipment adequately and we trust EXCEL to do SD .
So ? ? ?</tokentext>
<sentencetext>My doctor was explaining to me that my blood sugar readings should not have a standard deviation of more than 1/3rd of the average blood sugar reading.
Just to test if he knew what it meant, I asked him what a standard deviation was.
Oh the fun when he tried to bullshit his way out of that one!
He eventually told me that when I plot my data in Excel I can ask it to give me statistics on the column and it would mention what the standard deviation value was.
But when I pressed on and asked him what a standard deviation is, he shooed me off and told me to go look it up.
Never did he confess that he had no clue.I think you a being silly.
The core of your doctor-patient relationship should be you trust he has the knowledge and judgement to treat your condition in a professional and considered manner.
If he does not understand the statistics, it should not be a concern as many primary practitioners are not specialists, especially arcane things like deriving standard deviation.
Doctors are typically foremost clinicians, not chemists, mathematicians, grammar freaks, computer geeks etc... If you are not comfortable with your doctor, find another.
Anyway, doctors should follow sound scientific principals in their treatment but should NOT treat on the basis of numbers alone.
It is enough other trusted and respected specialists distil the relevant information and say "this is best practice in this situation".
This is usually called CME (continuing medical education).
Access to literature on the internet should allow you read up on whether the regimen you are on is reasonable or not.
Be aware, you WILL find published articles FOR and AGAINST any kind of treatment in medical journals and the REAL skill is knowing the context and the clinical biases that can occur and whether a particular therapy is appropriate.
Diseases or conditions rarely exist in total isolation or as a single entity.
Doctors have enough on their daily plate without other concerns in other areas.
We TRUST nurses will take accurate readings of temp, BP etc, we trust pharmacists will dispense correct dosage, we trust OT to sterilise equipment adequately and we trust EXCEL to do SD.
So???
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518402</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518784</id>
	<title>A Well Known Fact</title>
	<author>rlp</author>
	<datestamp>1268843640000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>87.24\% of statistics are made up.</p></htmltext>
<tokenext>87.24 \ % of statistics are made up .</tokentext>
<sentencetext>87.24\% of statistics are made up.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521496</id>
	<title>Re:Personal experience</title>
	<author>Rockoon</author>
	<datestamp>1268920560000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Hello Mr Profession.<br>
<br>
If you had to recommend only one textbook to a person more interested in the understanding (as opposed to the application) of statistics and statistical methods (they wish to "grok"), what would it be?</htmltext>
<tokenext>Hello Mr Profession .
If you had to recommend only one textbook to a person more interested in the understanding ( as opposed to the application ) of statistics and statistical methods ( they wish to " grok " ) , what would it be ?</tokentext>
<sentencetext>Hello Mr Profession.
If you had to recommend only one textbook to a person more interested in the understanding (as opposed to the application) of statistics and statistical methods (they wish to "grok"), what would it be?</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518718</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519290</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Tromad</author>
	<datestamp>1268849220000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>I was in school to become a psychiatrist when I noticed the same thing and dodged a bullet. And it wasn't even the fact that everything was stats, it was also that all the stats were terrible. There is also a huge discrepancy between statistical significance and effectiveness, which the entire industry seems to not understand.</p></htmltext>
<tokenext>I was in school to become a psychiatrist when I noticed the same thing and dodged a bullet .
And it was n't even the fact that everything was stats , it was also that all the stats were terrible .
There is also a huge discrepancy between statistical significance and effectiveness , which the entire industry seems to not understand .</tokentext>
<sentencetext>I was in school to become a psychiatrist when I noticed the same thing and dodged a bullet.
And it wasn't even the fact that everything was stats, it was also that all the stats were terrible.
There is also a huge discrepancy between statistical significance and effectiveness, which the entire industry seems to not understand.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31522354</id>
	<title>Re:PhD Candidate in Biostatistics Here</title>
	<author>PineHall</author>
	<datestamp>1268924880000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>It sounds like you are a Truth Seeker who has become jaded because of the basic assumptions underlying science and because our broken human nature does not always treat the scientific results properly.  You are pointing out the mess we are in and how Naturalism does not solve the problems.  I would encourage you to continue to seek to understand Reality/Truth.  It is important.  I found that the Christian Faith fits reality best.  Consider it.  There are presuppositions/assumptions also with Christianity but I believe it does explain reality best and science can fit into that Christian framework.</htmltext>
<tokenext>It sounds like you are a Truth Seeker who has become jaded because of the basic assumptions underlying science and because our broken human nature does not always treat the scientific results properly .
You are pointing out the mess we are in and how Naturalism does not solve the problems .
I would encourage you to continue to seek to understand Reality/Truth .
It is important .
I found that the Christian Faith fits reality best .
Consider it .
There are presuppositions/assumptions also with Christianity but I believe it does explain reality best and science can fit into that Christian framework .</tokentext>
<sentencetext>It sounds like you are a Truth Seeker who has become jaded because of the basic assumptions underlying science and because our broken human nature does not always treat the scientific results properly.
You are pointing out the mess we are in and how Naturalism does not solve the problems.
I would encourage you to continue to seek to understand Reality/Truth.
It is important.
I found that the Christian Faith fits reality best.
Consider it.
There are presuppositions/assumptions also with Christianity but I believe it does explain reality best and science can fit into that Christian framework.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518634</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518692</id>
	<title>only in medicine</title>
	<author>rook166</author>
	<datestamp>1268842800000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>5</modscore>
	<htmltext>In reading a couple of these types of articles recently I've noticed that the articles always talk about this being a problem across all journals, but only seem to mention a couple of different disciplines - medicine usually chief among them. Has anyone heard/read anything naming a hard science (e.g. chemistry or physics) as full of bad stats? My hunch is that this happens most often in medicine because you have the combination of controlling for a lot of variables as well as inadequate mathematics training.</htmltext>
<tokenext>In reading a couple of these types of articles recently I 've noticed that the articles always talk about this being a problem across all journals , but only seem to mention a couple of different disciplines - medicine usually chief among them .
Has anyone heard/read anything naming a hard science ( e.g .
chemistry or physics ) as full of bad stats ?
My hunch is that this happens most often in medicine because you have the combination of controlling for a lot of variables as well as inadequate mathematics training .</tokentext>
<sentencetext>In reading a couple of these types of articles recently I've noticed that the articles always talk about this being a problem across all journals, but only seem to mention a couple of different disciplines - medicine usually chief among them.
Has anyone heard/read anything naming a hard science (e.g.
chemistry or physics) as full of bad stats?
My hunch is that this happens most often in medicine because you have the combination of controlling for a lot of variables as well as inadequate mathematics training.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518636</id>
	<title>Probability Theory: The Logic of Science</title>
	<author>DuncanFoley</author>
	<datestamp>1268842440000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>The clearest discussion of the logic of probability reasoning I know of is E.T. Jaynes' Probability Theory: The Logic of Science. (Cambridge University Press).

Many of Jaynes' excellent papers on statistics are downloadable from <a href="http://bayes.wustl.edu/etj/etj.html" title="wustl.edu" rel="nofollow">http://bayes.wustl.edu/etj/etj.html</a> [wustl.edu].</htmltext>
<tokenext>The clearest discussion of the logic of probability reasoning I know of is E.T .
Jaynes ' Probability Theory : The Logic of Science .
( Cambridge University Press ) .
Many of Jaynes ' excellent papers on statistics are downloadable from http : //bayes.wustl.edu/etj/etj.html [ wustl.edu ] .</tokentext>
<sentencetext>The clearest discussion of the logic of probability reasoning I know of is E.T.
Jaynes' Probability Theory: The Logic of Science.
(Cambridge University Press).
Many of Jaynes' excellent papers on statistics are downloadable from http://bayes.wustl.edu/etj/etj.html [wustl.edu].</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519756</id>
	<title>circumcision research is full of faulty statistics</title>
	<author>Anonymous</author>
	<datestamp>1268943180000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>All these studies in Africa show that circumcision prevents aids.  However, if you look, Scandinavian countries have the lowest rates of aids and virtually no circumcision.</p><p>The US has a high circ rate and a high aids rate.</p><p>The reason Africa has those studies showing circumcision reduces aids is because after being cut you are laid up and can't boink!!</p></htmltext>
<tokenext>All these studies in Africa show that circumcision prevents aids .
However , if you look , Scandinavian countries have the lowest rates of aids and virtually no circumcision.The US has a high circ rate and a high aids rate.The reason Africa has those studies showing circumcision reduces aids is because after being cut you are laid up and ca n't boink !
!</tokentext>
<sentencetext>All these studies in Africa show that circumcision prevents aids.
However, if you look, Scandinavian countries have the lowest rates of aids and virtually no circumcision.The US has a high circ rate and a high aids rate.The reason Africa has those studies showing circumcision reduces aids is because after being cut you are laid up and can't boink!
!</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519824</id>
	<title>And 50\% is pulled out of ...</title>
	<author>freaker\_TuC</author>
	<datestamp>1268944500000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>.. a place where the sun doesn't shine (often - statistically), does that mean 100\% of those are stinky?</p></htmltext>
<tokenext>.. a place where the sun does n't shine ( often - statistically ) , does that mean 100 \ % of those are stinky ?</tokentext>
<sentencetext>.. a place where the sun doesn't shine (often - statistically), does that mean 100\% of those are stinky?</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518784</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31531984</id>
	<title>Re:Example: Standard Deviation</title>
	<author>JumpDrive</author>
	<datestamp>1268925000000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Oh and I actually made it through graduate school, maybe I should tell UT I want my money back.</htmltext>
<tokenext>Oh and I actually made it through graduate school , maybe I should tell UT I want my money back .</tokentext>
<sentencetext>Oh and I actually made it through graduate school, maybe I should tell UT I want my money back.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31523508</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518918</id>
	<title>Re:Personal experience</title>
	<author>fermion</author>
	<datestamp>1268844720000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><i>Remember that we're doctors, not mathematicians</i>
<p>
Would a doctor admitted that he or she could not read past a tenth grade level?  I think not.  Yet I am amazed at the number of apparently educated people who are willing, even to the point of being proud, of the fact that they cannot do anything basic high school math.
</p><p>
Statistics is a very difficult subject.  I have taken several courses and still cannot tell you the when to use a Paisson or Binomial distribution, but I do know the basics.  For instance, most naturally occurring variable be frequency distributed according to a normal distribution.  The key idea is that the variable is random.  What this means in terms of medical studies is that the participants are chosen randomly.  Defining how random a variable is a particularly hairy, yet important, part of statistics.  If the sample is random, and representative, then the result are crap.
</p><p>
I know enough doctors and medical researchers to know that the statics illiteracy is universal.  There are relatively simple books that explain much of what a researcher must know(I can't recall the names, but researchers around the hospital probably can recommend one).  And, like I tell students, it is possible to know whether the results of a calculator, or SPSS, is reasonable.</p></htmltext>
<tokenext>Remember that we 're doctors , not mathematicians Would a doctor admitted that he or she could not read past a tenth grade level ?
I think not .
Yet I am amazed at the number of apparently educated people who are willing , even to the point of being proud , of the fact that they can not do anything basic high school math .
Statistics is a very difficult subject .
I have taken several courses and still can not tell you the when to use a Paisson or Binomial distribution , but I do know the basics .
For instance , most naturally occurring variable be frequency distributed according to a normal distribution .
The key idea is that the variable is random .
What this means in terms of medical studies is that the participants are chosen randomly .
Defining how random a variable is a particularly hairy , yet important , part of statistics .
If the sample is random , and representative , then the result are crap .
I know enough doctors and medical researchers to know that the statics illiteracy is universal .
There are relatively simple books that explain much of what a researcher must know ( I ca n't recall the names , but researchers around the hospital probably can recommend one ) .
And , like I tell students , it is possible to know whether the results of a calculator , or SPSS , is reasonable .</tokentext>
<sentencetext>Remember that we're doctors, not mathematicians

Would a doctor admitted that he or she could not read past a tenth grade level?
I think not.
Yet I am amazed at the number of apparently educated people who are willing, even to the point of being proud, of the fact that they cannot do anything basic high school math.
Statistics is a very difficult subject.
I have taken several courses and still cannot tell you the when to use a Paisson or Binomial distribution, but I do know the basics.
For instance, most naturally occurring variable be frequency distributed according to a normal distribution.
The key idea is that the variable is random.
What this means in terms of medical studies is that the participants are chosen randomly.
Defining how random a variable is a particularly hairy, yet important, part of statistics.
If the sample is random, and representative, then the result are crap.
I know enough doctors and medical researchers to know that the statics illiteracy is universal.
There are relatively simple books that explain much of what a researcher must know(I can't recall the names, but researchers around the hospital probably can recommend one).
And, like I tell students, it is possible to know whether the results of a calculator, or SPSS, is reasonable.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518484</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519830</id>
	<title>Re:Summery?</title>
	<author>Saroful</author>
	<datestamp>1268944620000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>5</modscore>
	<htmltext>And what's the law about spelling/grammar corrections that incorrectly correct the supposed spelling error? (Redundancy is purposefully deliberate.)


"Its" is possessive. "It's" is a contraction of "it" and "is".



--
This has been a message from your friendly neighborhood Spelling Nazi.</htmltext>
<tokenext>And what 's the law about spelling/grammar corrections that incorrectly correct the supposed spelling error ?
( Redundancy is purposefully deliberate .
) " Its " is possessive .
" It 's " is a contraction of " it " and " is " .
-- This has been a message from your friendly neighborhood Spelling Nazi .</tokentext>
<sentencetext>And what's the law about spelling/grammar corrections that incorrectly correct the supposed spelling error?
(Redundancy is purposefully deliberate.
)


"Its" is possessive.
"It's" is a contraction of "it" and "is".
--
This has been a message from your friendly neighborhood Spelling Nazi.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518672</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31524306</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268934180000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Psychiatrists are Doctors (Medical Doctors), I believe you meant Psychologists, who are not Medical Doctors.</p></htmltext>
<tokenext>Psychiatrists are Doctors ( Medical Doctors ) , I believe you meant Psychologists , who are not Medical Doctors .</tokentext>
<sentencetext>Psychiatrists are Doctors (Medical Doctors), I believe you meant Psychologists, who are not Medical Doctors.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519170</id>
	<title>Re:What it actually said</title>
	<author>crmarvin42</author>
	<datestamp>1268847600000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p> If you chose a random set of conference proceedings, it is almost certain you will find at least one paper (and I suspect usually a dozen or more) that have statistical mistakes in them.</p></div><p> As someone who is sitting in a hotel room after the Midwest ASAS meeting in Des Moines, IA, I can personally attest to seeing improper statistics in the majority of the presentations I saw between 9 and 11am.  There were at least 7 presentations in which they "Double Dipped" by running orthogonal contrasts (linear &amp; quadratic) and mulitple comparison of simple effects (Tukey's HSD) on the same dataset with alpha = 0.05 for each type of test.</p></div>
	</htmltext>
<tokenext>If you chose a random set of conference proceedings , it is almost certain you will find at least one paper ( and I suspect usually a dozen or more ) that have statistical mistakes in them .
As someone who is sitting in a hotel room after the Midwest ASAS meeting in Des Moines , IA , I can personally attest to seeing improper statistics in the majority of the presentations I saw between 9 and 11am .
There were at least 7 presentations in which they " Double Dipped " by running orthogonal contrasts ( linear &amp; quadratic ) and mulitple comparison of simple effects ( Tukey 's HSD ) on the same dataset with alpha = 0.05 for each type of test .</tokentext>
<sentencetext> If you chose a random set of conference proceedings, it is almost certain you will find at least one paper (and I suspect usually a dozen or more) that have statistical mistakes in them.
As someone who is sitting in a hotel room after the Midwest ASAS meeting in Des Moines, IA, I can personally attest to seeing improper statistics in the majority of the presentations I saw between 9 and 11am.
There were at least 7 presentations in which they "Double Dipped" by running orthogonal contrasts (linear &amp; quadratic) and mulitple comparison of simple effects (Tukey's HSD) on the same dataset with alpha = 0.05 for each type of test.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518818</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31523508</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268930460000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>&gt;<br>&gt; Back when I was in graduate school me and my colleagues<nobr> <wbr></nobr>...<br>&gt;</p><p>Using an object pronoun as the subject of a sentence is inexcusable grammar and not at all appropriate for someone who has made it all the way to graduate school.</p><p>It's not just statistics that is misapplied.  Grammar may not be as critical as scientific statistics, but its misapplication still strongly indicates a failed overall education.</p></htmltext>
<tokenext>&gt; &gt; Back when I was in graduate school me and my colleagues ... &gt; Using an object pronoun as the subject of a sentence is inexcusable grammar and not at all appropriate for someone who has made it all the way to graduate school.It 's not just statistics that is misapplied .
Grammar may not be as critical as scientific statistics , but its misapplication still strongly indicates a failed overall education .</tokentext>
<sentencetext>&gt;&gt; Back when I was in graduate school me and my colleagues ...&gt;Using an object pronoun as the subject of a sentence is inexcusable grammar and not at all appropriate for someone who has made it all the way to graduate school.It's not just statistics that is misapplied.
Grammar may not be as critical as scientific statistics, but its misapplication still strongly indicates a failed overall education.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518852</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518398</id>
	<title>I dated a short, summery girl once</title>
	<author>Chess Piece Face</author>
	<datestamp>1268840400000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>She was like a little ball of sunshine.</p><p>As for statistics, does this really surprise anyone in a time when net polls are being reported as hard news?</p></htmltext>
<tokenext>She was like a little ball of sunshine.As for statistics , does this really surprise anyone in a time when net polls are being reported as hard news ?</tokentext>
<sentencetext>She was like a little ball of sunshine.As for statistics, does this really surprise anyone in a time when net polls are being reported as hard news?</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31523472</id>
	<title>What my statistics professor taught me</title>
	<author>ElmoGonzo</author>
	<datestamp>1268930280000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Thou shalt not worship the<nobr> <wbr></nobr>.05 level.

Correlation does not imply causation -- you need to have some idea of HOW the values are correlated.

Linear regression is only valid when the relationship is in fact linear.

The more variables added to a multivariate statistical model, the greater the likelihood that there will be a spurious correlation.

SPSS will always find something when you tell it to look hard enough.</htmltext>
<tokenext>Thou shalt not worship the .05 level .
Correlation does not imply causation -- you need to have some idea of HOW the values are correlated .
Linear regression is only valid when the relationship is in fact linear .
The more variables added to a multivariate statistical model , the greater the likelihood that there will be a spurious correlation .
SPSS will always find something when you tell it to look hard enough .</tokentext>
<sentencetext>Thou shalt not worship the .05 level.
Correlation does not imply causation -- you need to have some idea of HOW the values are correlated.
Linear regression is only valid when the relationship is in fact linear.
The more variables added to a multivariate statistical model, the greater the likelihood that there will be a spurious correlation.
SPSS will always find something when you tell it to look hard enough.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519708</id>
	<title>Significance is NOT probabilty</title>
	<author>drewhk</author>
	<datestamp>1268942460000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>2</modscore>
	<htmltext><p>.. or at least not the probability of the hypothesis. This is one of the errors that people make. Having 0.95 significance do NOT imply having 95\% chance for the hypothesis being true! The significance is the probability of the test outcome assuming the hypothesis is true (in other words it is a likelihood value). You have to multiply it by a prior to obtain real probabilities.</p><p>Significance values will not even add up to 1 over the two hypothesises!</p><p>The root of the problem is that frequentists can not use probabilities for statements -- only for events. In frequentist terms you have to have a sigma algebra over some Omega state space which is measurable. Bayesians on the other hand can talk about the probabilities of any statements using probability theory as an extension of formal logic. I really recommend reading the books of E. T Jeynes and David McKay.</p><p>Other false assumptions people make with statistics:<br>
&nbsp; - Everything is normally distributed<br>
&nbsp; - Everything has a variance<br>
&nbsp; - Everything has an expected value<br>
&nbsp; - Hypothesis testing is without bias (in fact it is equivalent to give 50\% prior probability to both hypothesises)<br>
&nbsp; - Variance means average distance from mean<br>
&nbsp; - Empirical variance does not have a variance</p></htmltext>
<tokenext>.. or at least not the probability of the hypothesis .
This is one of the errors that people make .
Having 0.95 significance do NOT imply having 95 \ % chance for the hypothesis being true !
The significance is the probability of the test outcome assuming the hypothesis is true ( in other words it is a likelihood value ) .
You have to multiply it by a prior to obtain real probabilities.Significance values will not even add up to 1 over the two hypothesises ! The root of the problem is that frequentists can not use probabilities for statements -- only for events .
In frequentist terms you have to have a sigma algebra over some Omega state space which is measurable .
Bayesians on the other hand can talk about the probabilities of any statements using probability theory as an extension of formal logic .
I really recommend reading the books of E. T Jeynes and David McKay.Other false assumptions people make with statistics :   - Everything is normally distributed   - Everything has a variance   - Everything has an expected value   - Hypothesis testing is without bias ( in fact it is equivalent to give 50 \ % prior probability to both hypothesises )   - Variance means average distance from mean   - Empirical variance does not have a variance</tokentext>
<sentencetext>.. or at least not the probability of the hypothesis.
This is one of the errors that people make.
Having 0.95 significance do NOT imply having 95\% chance for the hypothesis being true!
The significance is the probability of the test outcome assuming the hypothesis is true (in other words it is a likelihood value).
You have to multiply it by a prior to obtain real probabilities.Significance values will not even add up to 1 over the two hypothesises!The root of the problem is that frequentists can not use probabilities for statements -- only for events.
In frequentist terms you have to have a sigma algebra over some Omega state space which is measurable.
Bayesians on the other hand can talk about the probabilities of any statements using probability theory as an extension of formal logic.
I really recommend reading the books of E. T Jeynes and David McKay.Other false assumptions people make with statistics:
  - Everything is normally distributed
  - Everything has a variance
  - Everything has an expected value
  - Hypothesis testing is without bias (in fact it is equivalent to give 50\% prior probability to both hypothesises)
  - Variance means average distance from mean
  - Empirical variance does not have a variance</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519286</id>
	<title>Re:Lies, Damned Lies, and Statistics.</title>
	<author>jpate</author>
	<datestamp>1268849160000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Depends on your definition of expectation<nobr> <wbr></nobr>;)
<br> <br>
maximum likelihood isn't the only way to go.... and sparse data is <b>exactly</b> where it crashes and burns</htmltext>
<tokenext>Depends on your definition of expectation ; ) maximum likelihood is n't the only way to go.... and sparse data is exactly where it crashes and burns</tokentext>
<sentencetext>Depends on your definition of expectation ;)
 
maximum likelihood isn't the only way to go.... and sparse data is exactly where it crashes and burns</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518850</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518776</id>
	<title>Re:Lies, Damned Lies, and Statistics.</title>
	<author>Opportunist</author>
	<datestamp>1268843580000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>While we're at it, stay away from hospitals! Most people in civilised countries die there rather than anywhere else!</p></htmltext>
<tokenext>While we 're at it , stay away from hospitals !
Most people in civilised countries die there rather than anywhere else !</tokentext>
<sentencetext>While we're at it, stay away from hospitals!
Most people in civilised countries die there rather than anywhere else!</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518344</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519546</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Opportunist</author>
	<datestamp>1268853180000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Oh, the industry understands it just fine. They also understand that claiming something is "effective in treating X" means that they can make a fortune with that something.</p><p>That is, if the distance between cost to produce and price is big enough. If it isn't, it will certainly be not effective at treating anything.</p></htmltext>
<tokenext>Oh , the industry understands it just fine .
They also understand that claiming something is " effective in treating X " means that they can make a fortune with that something.That is , if the distance between cost to produce and price is big enough .
If it is n't , it will certainly be not effective at treating anything .</tokentext>
<sentencetext>Oh, the industry understands it just fine.
They also understand that claiming something is "effective in treating X" means that they can make a fortune with that something.That is, if the distance between cost to produce and price is big enough.
If it isn't, it will certainly be not effective at treating anything.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519290</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31522988</id>
	<title>Now there's a surprise....</title>
	<author>ibm1130</author>
	<datestamp>1268927880000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Yeah, the mathematical statistics courses were just chock full of what we called "meds keeners" or "hoovers" ie those seeking admission to med school. Even those majoring in alleged sciences like biology were often shockingly ignorant of hard sciences and tended to fulfill only the minimum requirements in things like chemistry.</p></htmltext>
<tokenext>Yeah , the mathematical statistics courses were just chock full of what we called " meds keeners " or " hoovers " ie those seeking admission to med school .
Even those majoring in alleged sciences like biology were often shockingly ignorant of hard sciences and tended to fulfill only the minimum requirements in things like chemistry .</tokentext>
<sentencetext>Yeah, the mathematical statistics courses were just chock full of what we called "meds keeners" or "hoovers" ie those seeking admission to med school.
Even those majoring in alleged sciences like biology were often shockingly ignorant of hard sciences and tended to fulfill only the minimum requirements in things like chemistry.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518526</id>
	<title>Re:Long winded troll</title>
	<author>Anonymous</author>
	<datestamp>1268841480000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>science is not in the bussiness of proof</p></div><p>So what is it in the business of?</p></div>
	</htmltext>
<tokenext>science is not in the bussiness of proofSo what is it in the business of ?</tokentext>
<sentencetext>science is not in the bussiness of proofSo what is it in the business of?
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518396</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31522060</id>
	<title>statistical mechanics</title>
	<author>pigwiggle</author>
	<datestamp>1268923560000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>I do stat mech.  Most of the papers I read pay very little attention to assigning a level statistical significance to their "measurements".  When they do, assumptions of uncorrelated measurements are always made - and probably incorrectly.  I struggle with the statistics myself.  I find myself working out of my undergraduate stats text mostly.  I feel I'm more concerned with understanding how statistically meaningful  my measurements are than most of my colleagues.  And I worry about my understanding of the statistical methods I use.</p></htmltext>
<tokenext>I do stat mech .
Most of the papers I read pay very little attention to assigning a level statistical significance to their " measurements " .
When they do , assumptions of uncorrelated measurements are always made - and probably incorrectly .
I struggle with the statistics myself .
I find myself working out of my undergraduate stats text mostly .
I feel I 'm more concerned with understanding how statistically meaningful my measurements are than most of my colleagues .
And I worry about my understanding of the statistical methods I use .</tokentext>
<sentencetext>I do stat mech.
Most of the papers I read pay very little attention to assigning a level statistical significance to their "measurements".
When they do, assumptions of uncorrelated measurements are always made - and probably incorrectly.
I struggle with the statistics myself.
I find myself working out of my undergraduate stats text mostly.
I feel I'm more concerned with understanding how statistically meaningful  my measurements are than most of my colleagues.
And I worry about my understanding of the statistical methods I use.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518692</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31533206</id>
	<title>Re:only in medicine</title>
	<author>Anonymous</author>
	<datestamp>1268936760000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Statistics is used in those sciences for which we don't have a physical model -- basically all sciences studying living beings in general and people in particular. People sciences include everything from medicine through psychiatry and stock markets.</p><p>I'm not saying it's a bad thing -- if you have little or no idea how something works, at least try to make some utility of it and predict it with statistics. Problem is, as the article says, such statistics (when done correctly, which they also say often is not) is too soon claimed to represent a scientific truth, by people hungry for fame and/or money.</p></htmltext>
<tokenext>Statistics is used in those sciences for which we do n't have a physical model -- basically all sciences studying living beings in general and people in particular .
People sciences include everything from medicine through psychiatry and stock markets.I 'm not saying it 's a bad thing -- if you have little or no idea how something works , at least try to make some utility of it and predict it with statistics .
Problem is , as the article says , such statistics ( when done correctly , which they also say often is not ) is too soon claimed to represent a scientific truth , by people hungry for fame and/or money .</tokentext>
<sentencetext>Statistics is used in those sciences for which we don't have a physical model -- basically all sciences studying living beings in general and people in particular.
People sciences include everything from medicine through psychiatry and stock markets.I'm not saying it's a bad thing -- if you have little or no idea how something works, at least try to make some utility of it and predict it with statistics.
Problem is, as the article says, such statistics (when done correctly, which they also say often is not) is too soon claimed to represent a scientific truth, by people hungry for fame and/or money.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518692</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518512</id>
	<title>Re:Its common knowledge</title>
	<author>Anonymous</author>
	<datestamp>1268841420000</datestamp>
	<modclass>Troll</modclass>
	<modscore>-1</modscore>
	<htmltext>That makes sense. <br> <br>

I read that the percentage of niggers who have herpes was around 50\%, but I think it's more around 110\% with a 10\% margin of error. As I recall, there was an additional complication involving the methods of that study -- the standard deviation was difficult to quantify because <i>all</i> niggers are deviants -- and the graphs looked more like a uniform distribution than a standard bell-curve.</htmltext>
<tokenext>That makes sense .
I read that the percentage of niggers who have herpes was around 50 \ % , but I think it 's more around 110 \ % with a 10 \ % margin of error .
As I recall , there was an additional complication involving the methods of that study -- the standard deviation was difficult to quantify because all niggers are deviants -- and the graphs looked more like a uniform distribution than a standard bell-curve .</tokentext>
<sentencetext>That makes sense.
I read that the percentage of niggers who have herpes was around 50\%, but I think it's more around 110\% with a 10\% margin of error.
As I recall, there was an additional complication involving the methods of that study -- the standard deviation was difficult to quantify because all niggers are deviants -- and the graphs looked more like a uniform distribution than a standard bell-curve.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518348</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518522</id>
	<title>Statistical assumptions are often ignored</title>
	<author>Anonymous</author>
	<datestamp>1268841420000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>1</modscore>
	<htmltext><p>Statistical methods are typically developed for fairly specific mathematical models. A practitioner may error greatly by using a statistical method outside of its intended purview. For example, many statistical tests assume that different groups of observations are independent or correlated in a specific way. If this isn't true then the resulting inferences can be very inaccurate.</p><p>Unfortunately the spread of "easy to use" statistical software is making this problem worse. Many scientists just enter their data and select an analysis from a drop-down menu - thinking that just because their data is in the right format that the results will accurate.  It would be better if people had to think about what analysis to choose rather than just treating the choice of a test like the choice of a visual effect in photoshop.</p><p>IAAS (statistician), for what it's worth...</p></htmltext>
<tokenext>Statistical methods are typically developed for fairly specific mathematical models .
A practitioner may error greatly by using a statistical method outside of its intended purview .
For example , many statistical tests assume that different groups of observations are independent or correlated in a specific way .
If this is n't true then the resulting inferences can be very inaccurate.Unfortunately the spread of " easy to use " statistical software is making this problem worse .
Many scientists just enter their data and select an analysis from a drop-down menu - thinking that just because their data is in the right format that the results will accurate .
It would be better if people had to think about what analysis to choose rather than just treating the choice of a test like the choice of a visual effect in photoshop.IAAS ( statistician ) , for what it 's worth.. .</tokentext>
<sentencetext>Statistical methods are typically developed for fairly specific mathematical models.
A practitioner may error greatly by using a statistical method outside of its intended purview.
For example, many statistical tests assume that different groups of observations are independent or correlated in a specific way.
If this isn't true then the resulting inferences can be very inaccurate.Unfortunately the spread of "easy to use" statistical software is making this problem worse.
Many scientists just enter their data and select an analysis from a drop-down menu - thinking that just because their data is in the right format that the results will accurate.
It would be better if people had to think about what analysis to choose rather than just treating the choice of a test like the choice of a visual effect in photoshop.IAAS (statistician), for what it's worth...</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519396</id>
	<title>Re:What it actually said</title>
	<author>tabdelgawad</author>
	<datestamp>1268850600000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>2</modscore>
	<htmltext><p>Good summary, but I call bullshit on the article.  Most of the problems you mention and the others in the article are common popular misinterpretations of statistical results, but that doesn't mean they're common mistakes made by researchers in the studies themselves.  Any rookie peer-reviewer would spot them immediately if they ever make it into a manuscript.</p><p>This doesn't mean that there aren't a lot of bad statistics-based studies out there, especially in medicine.  But the problems are usually much more subtle than the article implies.  Standard statistical methods require many regularity and sampling assumptions to be valid, and a lot of times researchers take these assumptions for granted when even a little probing would show that they're violated.  A lot of advances in recent econometrics have been in the development of robust methods (valid when standard assumptions are violated), and those advances unfortunately take a long time to filter down to the 'applied researcher' level.  If you're an applied researcher, it's generally unlikely you'll use statistical advances you didn't learn as a grad student.</p><p>And frankly, I have no idea what the Frequentist/Bayesian debate has to do with any of this.  To suggest that using Bayesian methods is some sort of solution for the problems listed in the article is ridiculous.</p></htmltext>
<tokenext>Good summary , but I call bullshit on the article .
Most of the problems you mention and the others in the article are common popular misinterpretations of statistical results , but that does n't mean they 're common mistakes made by researchers in the studies themselves .
Any rookie peer-reviewer would spot them immediately if they ever make it into a manuscript.This does n't mean that there are n't a lot of bad statistics-based studies out there , especially in medicine .
But the problems are usually much more subtle than the article implies .
Standard statistical methods require many regularity and sampling assumptions to be valid , and a lot of times researchers take these assumptions for granted when even a little probing would show that they 're violated .
A lot of advances in recent econometrics have been in the development of robust methods ( valid when standard assumptions are violated ) , and those advances unfortunately take a long time to filter down to the 'applied researcher ' level .
If you 're an applied researcher , it 's generally unlikely you 'll use statistical advances you did n't learn as a grad student.And frankly , I have no idea what the Frequentist/Bayesian debate has to do with any of this .
To suggest that using Bayesian methods is some sort of solution for the problems listed in the article is ridiculous .</tokentext>
<sentencetext>Good summary, but I call bullshit on the article.
Most of the problems you mention and the others in the article are common popular misinterpretations of statistical results, but that doesn't mean they're common mistakes made by researchers in the studies themselves.
Any rookie peer-reviewer would spot them immediately if they ever make it into a manuscript.This doesn't mean that there aren't a lot of bad statistics-based studies out there, especially in medicine.
But the problems are usually much more subtle than the article implies.
Standard statistical methods require many regularity and sampling assumptions to be valid, and a lot of times researchers take these assumptions for granted when even a little probing would show that they're violated.
A lot of advances in recent econometrics have been in the development of robust methods (valid when standard assumptions are violated), and those advances unfortunately take a long time to filter down to the 'applied researcher' level.
If you're an applied researcher, it's generally unlikely you'll use statistical advances you didn't learn as a grad student.And frankly, I have no idea what the Frequentist/Bayesian debate has to do with any of this.
To suggest that using Bayesian methods is some sort of solution for the problems listed in the article is ridiculous.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518818</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519700</id>
	<title>Re:Personal experience</title>
	<author>hazem</author>
	<datestamp>1268855940000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><i>And then it struck me - most of the research I had read had applied parametric statistical tests to their data - that it, the researchers made an assumption that the underlying distribution of results would fall on a normal curve.</i></p><p>It seems I heard a radio interview recently that discussed how many of the mathematical models used on Wall Street make the same error.  That the probability of events don't actually follow a normal distribution, so high-impact anomalous events that would normally be in the very thin parts of the normal curve are actually in a thicker part of some other curve... and thus more likely to happen.</p><p>I wish I could remember the interview.</p></htmltext>
<tokenext>And then it struck me - most of the research I had read had applied parametric statistical tests to their data - that it , the researchers made an assumption that the underlying distribution of results would fall on a normal curve.It seems I heard a radio interview recently that discussed how many of the mathematical models used on Wall Street make the same error .
That the probability of events do n't actually follow a normal distribution , so high-impact anomalous events that would normally be in the very thin parts of the normal curve are actually in a thicker part of some other curve... and thus more likely to happen.I wish I could remember the interview .</tokentext>
<sentencetext>And then it struck me - most of the research I had read had applied parametric statistical tests to their data - that it, the researchers made an assumption that the underlying distribution of results would fall on a normal curve.It seems I heard a radio interview recently that discussed how many of the mathematical models used on Wall Street make the same error.
That the probability of events don't actually follow a normal distribution, so high-impact anomalous events that would normally be in the very thin parts of the normal curve are actually in a thicker part of some other curve... and thus more likely to happen.I wish I could remember the interview.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518484</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518852</id>
	<title>Re:Example: Standard Deviation</title>
	<author>JumpDrive</author>
	<datestamp>1268844240000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>3</modscore>
	<htmltext>I agree with your concerns.  Being a chemical engineer and a physical scientist, I have often found medical doctors understanding of chemistry and other sciences lacking.  I once had an argument about chemical kinetics involved in a prescription drug I was taking,  he basically told me I didn't know what I was talking about and blew me off.  After another run in with him over another issue I fired him.  But that's just one of my personal issues with a doctor.<br> <br>
Back when I was in graduate school me and my colleagues in graduate science taught pre-med chemistry and physics, which was a really watered down version of chemistry and physics which were taught to engineers and science majors.  To be honest I thought it was kind of scary.  All these years I was taught that medical student were supposed to be the best and the brightest, but we spoon fed them "baby chemistry" and "baby physics".  <br> <br>
Since that time I have had many discussions with professors about this and they and I have come to the same conclusion, "the best and the brightest do not go into medical school".  Thirty or forty years ago this may have been true, but economics has taken a turn and it just isn't the case anymore.  <br> <br>
And why would they?  They can make more money on Wall Street, they don't have to hassle with bureaucracy of health insurance, they don't have to hassle with lawyers, so why would the best and brightest go into medicine.<br> <br>And you want to know what kind of income a hot little girl with a business degree can get. Pharmaceutical sales can pay 6 figures for one good figure.  So the next time you see that good looking girl pulling that bag through your doctors office realize she is probably making a lot of money.  More money than the average general practitioner .</htmltext>
<tokenext>I agree with your concerns .
Being a chemical engineer and a physical scientist , I have often found medical doctors understanding of chemistry and other sciences lacking .
I once had an argument about chemical kinetics involved in a prescription drug I was taking , he basically told me I did n't know what I was talking about and blew me off .
After another run in with him over another issue I fired him .
But that 's just one of my personal issues with a doctor .
Back when I was in graduate school me and my colleagues in graduate science taught pre-med chemistry and physics , which was a really watered down version of chemistry and physics which were taught to engineers and science majors .
To be honest I thought it was kind of scary .
All these years I was taught that medical student were supposed to be the best and the brightest , but we spoon fed them " baby chemistry " and " baby physics " .
Since that time I have had many discussions with professors about this and they and I have come to the same conclusion , " the best and the brightest do not go into medical school " .
Thirty or forty years ago this may have been true , but economics has taken a turn and it just is n't the case anymore .
And why would they ?
They can make more money on Wall Street , they do n't have to hassle with bureaucracy of health insurance , they do n't have to hassle with lawyers , so why would the best and brightest go into medicine .
And you want to know what kind of income a hot little girl with a business degree can get .
Pharmaceutical sales can pay 6 figures for one good figure .
So the next time you see that good looking girl pulling that bag through your doctors office realize she is probably making a lot of money .
More money than the average general practitioner .</tokentext>
<sentencetext>I agree with your concerns.
Being a chemical engineer and a physical scientist, I have often found medical doctors understanding of chemistry and other sciences lacking.
I once had an argument about chemical kinetics involved in a prescription drug I was taking,  he basically told me I didn't know what I was talking about and blew me off.
After another run in with him over another issue I fired him.
But that's just one of my personal issues with a doctor.
Back when I was in graduate school me and my colleagues in graduate science taught pre-med chemistry and physics, which was a really watered down version of chemistry and physics which were taught to engineers and science majors.
To be honest I thought it was kind of scary.
All these years I was taught that medical student were supposed to be the best and the brightest, but we spoon fed them "baby chemistry" and "baby physics".
Since that time I have had many discussions with professors about this and they and I have come to the same conclusion, "the best and the brightest do not go into medical school".
Thirty or forty years ago this may have been true, but economics has taken a turn and it just isn't the case anymore.
And why would they?
They can make more money on Wall Street, they don't have to hassle with bureaucracy of health insurance, they don't have to hassle with lawyers, so why would the best and brightest go into medicine.
And you want to know what kind of income a hot little girl with a business degree can get.
Pharmaceutical sales can pay 6 figures for one good figure.
So the next time you see that good looking girl pulling that bag through your doctors office realize she is probably making a lot of money.
More money than the average general practitioner .</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518402</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519896</id>
	<title>Re:Example: Standard Deviation</title>
	<author>fsterman</author>
	<datestamp>1268945520000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Odd how we have used double blind studies and statistics to tell us which treatments work the best, which is how we pulled ourselves out of the Freudian "They want to sit in their office, put people on their couch (or, more modern, in a comfy chair) and get 100 bucks an hour for listening to some idiot whine."</p><p>Oh, and most of us have to get a masters or PhD, so shove your "struggle with statistics" right where you got the rest of your information.</p></htmltext>
<tokenext>Odd how we have used double blind studies and statistics to tell us which treatments work the best , which is how we pulled ourselves out of the Freudian " They want to sit in their office , put people on their couch ( or , more modern , in a comfy chair ) and get 100 bucks an hour for listening to some idiot whine .
" Oh , and most of us have to get a masters or PhD , so shove your " struggle with statistics " right where you got the rest of your information .</tokentext>
<sentencetext>Odd how we have used double blind studies and statistics to tell us which treatments work the best, which is how we pulled ourselves out of the Freudian "They want to sit in their office, put people on their couch (or, more modern, in a comfy chair) and get 100 bucks an hour for listening to some idiot whine.
"Oh, and most of us have to get a masters or PhD, so shove your "struggle with statistics" right where you got the rest of your information.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518344</id>
	<title>Lies, Damned Lies, and Statistics.</title>
	<author>Shadow of Eternity</author>
	<datestamp>1268839860000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>5</modscore>
	<htmltext><p>In other news math may not lie but people still can, all the honesty and good statistics in the world doesnt help end-user stupidity, and there are statistically two popes per square kilometer in the vatican.</p></htmltext>
<tokenext>In other news math may not lie but people still can , all the honesty and good statistics in the world doesnt help end-user stupidity , and there are statistically two popes per square kilometer in the vatican .</tokentext>
<sentencetext>In other news math may not lie but people still can, all the honesty and good statistics in the world doesnt help end-user stupidity, and there are statistically two popes per square kilometer in the vatican.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521162</id>
	<title>Re:Personal experience</title>
	<author>sebaseba</author>
	<datestamp>1268918400000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Hey, would you maybe recommend a book on that: statistics which focuses more on concepts than formulas?</htmltext>
<tokenext>Hey , would you maybe recommend a book on that : statistics which focuses more on concepts than formulas ?</tokentext>
<sentencetext>Hey, would you maybe recommend a book on that: statistics which focuses more on concepts than formulas?</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518718</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519550</id>
	<title>Re:Summery?</title>
	<author>sincewhen</author>
	<datestamp>1268853240000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>I dont no about that law.</p><p>Anyway, thanks for the reminder - I drive with sigs off so I'd forgotten I had one.</p></htmltext>
<tokenext>I dont no about that law.Anyway , thanks for the reminder - I drive with sigs off so I 'd forgotten I had one .</tokentext>
<sentencetext>I dont no about that law.Anyway, thanks for the reminder - I drive with sigs off so I'd forgotten I had one.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518672</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520376</id>
	<title>Re:What it actually said</title>
	<author>Ardeaem</author>
	<datestamp>1268909700000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>"p &lt; 0.05" does not mean there is a 95\% chance of your result being "true"; it just means that someone else rolling dice has a 5\% chance of achieving the same result through chance alone.</p></div><p>Even this is not quite correct - it is wrong in a critical way. A p value includes the probability of obtaining not just the data you obtained, but all data which is "more extreme" than the data you obtained. For continuous distributions like the Normal distribution, the probability of achieving the "same result through chance alone" is literally 0, because the area under the normal curve at a point is 0. Misunderstanding this fact causes all sorts of problems.</p></div>
	</htmltext>
<tokenext>" p Even this is not quite correct - it is wrong in a critical way .
A p value includes the probability of obtaining not just the data you obtained , but all data which is " more extreme " than the data you obtained .
For continuous distributions like the Normal distribution , the probability of achieving the " same result through chance alone " is literally 0 , because the area under the normal curve at a point is 0 .
Misunderstanding this fact causes all sorts of problems .</tokentext>
<sentencetext>"p Even this is not quite correct - it is wrong in a critical way.
A p value includes the probability of obtaining not just the data you obtained, but all data which is "more extreme" than the data you obtained.
For continuous distributions like the Normal distribution, the probability of achieving the "same result through chance alone" is literally 0, because the area under the normal curve at a point is 0.
Misunderstanding this fact causes all sorts of problems.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518818</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31689508</id>
	<title>Re:Medicene / Science for money</title>
	<author>Uzuri</author>
	<datestamp>1270057920000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Because money grows on trees.  Duh.</p><p>Doesn't it?</p></htmltext>
<tokenext>Because money grows on trees .
Duh.Does n't it ?</tokentext>
<sentencetext>Because money grows on trees.
Duh.Doesn't it?</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520228</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519676</id>
	<title>Re:Personal experience</title>
	<author>Anonymous</author>
	<datestamp>1268855760000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Hehe.. I remember Simpson's Paradox from my stats courses...</p><p>There was something else I remember vaguely. It had to do with sampling. Supposedly there were situations where choosing a representative sample was more effective than polling 100\% of a population. I.e., if you had 1000 people, it would be better to choose x number to meet your confidence intervals, etc.. than it was to study every one in the population.  I remember the instructor speaking about it for a class but don't remember the details..  Any idea?? It bugs me occasionally and is apparently obscure enough that I can't find it on the Wolfram or Wikipedia sites (or I'm too dumb to figure the proper search term).</p></htmltext>
<tokenext>Hehe.. I remember Simpson 's Paradox from my stats courses...There was something else I remember vaguely .
It had to do with sampling .
Supposedly there were situations where choosing a representative sample was more effective than polling 100 \ % of a population .
I.e. , if you had 1000 people , it would be better to choose x number to meet your confidence intervals , etc.. than it was to study every one in the population .
I remember the instructor speaking about it for a class but do n't remember the details.. Any idea ? ?
It bugs me occasionally and is apparently obscure enough that I ca n't find it on the Wolfram or Wikipedia sites ( or I 'm too dumb to figure the proper search term ) .</tokentext>
<sentencetext>Hehe.. I remember Simpson's Paradox from my stats courses...There was something else I remember vaguely.
It had to do with sampling.
Supposedly there were situations where choosing a representative sample was more effective than polling 100\% of a population.
I.e., if you had 1000 people, it would be better to choose x number to meet your confidence intervals, etc.. than it was to study every one in the population.
I remember the instructor speaking about it for a class but don't remember the details..  Any idea??
It bugs me occasionally and is apparently obscure enough that I can't find it on the Wolfram or Wikipedia sites (or I'm too dumb to figure the proper search term).</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518718</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521458</id>
	<title>Re:Personal experience</title>
	<author>Anonymous</author>
	<datestamp>1268920320000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>There really should be a quantative value that can put a figure to the reliability of any given statistic. I mean it will never be 100\% but if people knew that a stat was made up from, and how said data was gathered then it could be rated.</p><p>So any bad statistical analysis would be pointless as people wouldn't trust the conclusions. Instead people would make sure their work actually covered the bases and thought about the problems involved, taking care to address ways that the data could be bias.</p></htmltext>
<tokenext>There really should be a quantative value that can put a figure to the reliability of any given statistic .
I mean it will never be 100 \ % but if people knew that a stat was made up from , and how said data was gathered then it could be rated.So any bad statistical analysis would be pointless as people would n't trust the conclusions .
Instead people would make sure their work actually covered the bases and thought about the problems involved , taking care to address ways that the data could be bias .</tokentext>
<sentencetext>There really should be a quantative value that can put a figure to the reliability of any given statistic.
I mean it will never be 100\% but if people knew that a stat was made up from, and how said data was gathered then it could be rated.So any bad statistical analysis would be pointless as people wouldn't trust the conclusions.
Instead people would make sure their work actually covered the bases and thought about the problems involved, taking care to address ways that the data could be bias.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518718</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520838</id>
	<title>The press are just as bad...</title>
	<author>Endophage</author>
	<datestamp>1268915280000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Apologies that I can't remember the exact details but I read about the case of a university professor in the US who lost his job for allegedly saying there were more men in science because men were more intelligent than women.  The issue revolved around the press not understanding standard deviations.  What the professor had actually said (in fewer words) was that the standard bell curve for intelligence is slightly difference by gender. For men it is shorter and fatter but the tails don't extend very far while for women the curve is taller but with very long tails.  It boils down to there being more intelligent men but equally, more stupid men while women have the potential to be both significantly more intelligent but also significantly less intelligent than the bulk of the male population.  <br> <br>

All the details are in the book Super Crunchers which is incidentally a fantastic read for anyone interested in the application of statistics in a very general, non-mathematical sense (it covers the use of statistics by baseball scouts, medical computers, predicting changes in flight prices and predicting wine vintages to name a few scenarios that are covered).  Unfortunately the professor lost his job because of the furore generated by the misinterpretation in the press.</htmltext>
<tokenext>Apologies that I ca n't remember the exact details but I read about the case of a university professor in the US who lost his job for allegedly saying there were more men in science because men were more intelligent than women .
The issue revolved around the press not understanding standard deviations .
What the professor had actually said ( in fewer words ) was that the standard bell curve for intelligence is slightly difference by gender .
For men it is shorter and fatter but the tails do n't extend very far while for women the curve is taller but with very long tails .
It boils down to there being more intelligent men but equally , more stupid men while women have the potential to be both significantly more intelligent but also significantly less intelligent than the bulk of the male population .
All the details are in the book Super Crunchers which is incidentally a fantastic read for anyone interested in the application of statistics in a very general , non-mathematical sense ( it covers the use of statistics by baseball scouts , medical computers , predicting changes in flight prices and predicting wine vintages to name a few scenarios that are covered ) .
Unfortunately the professor lost his job because of the furore generated by the misinterpretation in the press .</tokentext>
<sentencetext>Apologies that I can't remember the exact details but I read about the case of a university professor in the US who lost his job for allegedly saying there were more men in science because men were more intelligent than women.
The issue revolved around the press not understanding standard deviations.
What the professor had actually said (in fewer words) was that the standard bell curve for intelligence is slightly difference by gender.
For men it is shorter and fatter but the tails don't extend very far while for women the curve is taller but with very long tails.
It boils down to there being more intelligent men but equally, more stupid men while women have the potential to be both significantly more intelligent but also significantly less intelligent than the bulk of the male population.
All the details are in the book Super Crunchers which is incidentally a fantastic read for anyone interested in the application of statistics in a very general, non-mathematical sense (it covers the use of statistics by baseball scouts, medical computers, predicting changes in flight prices and predicting wine vintages to name a few scenarios that are covered).
Unfortunately the professor lost his job because of the furore generated by the misinterpretation in the press.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518574</id>
	<title>Re:Pirates cause cool weather</title>
	<author>Nefarious Wheel</author>
	<datestamp>1268841840000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Careful, sir or madam, with that graph you are treading dangerously close to a theological argument here.  Global Warming is the Flying Spaghetti Monster's way of telling us we need more pirates.  If you want to know exactly how pirates and global warming correlate, please send money, and we will lease you an AVOM (Awesome Volt-Ohm Meter) with a blank face with which you can scare yourself until your midichlorians take over your reflexes.<p>"Luke Skywalker's a Jedi of course;</p><p>And he's prone to have much intercourse;</p><p>So he calls up his Princess, to beg for some incest,</p><p>Grabs a blindfold and uses the Force.</p></htmltext>
<tokenext>Careful , sir or madam , with that graph you are treading dangerously close to a theological argument here .
Global Warming is the Flying Spaghetti Monster 's way of telling us we need more pirates .
If you want to know exactly how pirates and global warming correlate , please send money , and we will lease you an AVOM ( Awesome Volt-Ohm Meter ) with a blank face with which you can scare yourself until your midichlorians take over your reflexes .
" Luke Skywalker 's a Jedi of course ; And he 's prone to have much intercourse ; So he calls up his Princess , to beg for some incest,Grabs a blindfold and uses the Force .</tokentext>
<sentencetext>Careful, sir or madam, with that graph you are treading dangerously close to a theological argument here.
Global Warming is the Flying Spaghetti Monster's way of telling us we need more pirates.
If you want to know exactly how pirates and global warming correlate, please send money, and we will lease you an AVOM (Awesome Volt-Ohm Meter) with a blank face with which you can scare yourself until your midichlorians take over your reflexes.
"Luke Skywalker's a Jedi of course;And he's prone to have much intercourse;So he calls up his Princess, to beg for some incest,Grabs a blindfold and uses the Force.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518486</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519820</id>
	<title>Re:only in medicine</title>
	<author>daver00</author>
	<datestamp>1268944260000</datestamp>
	<modclass>Funny</modclass>
	<modscore>3</modscore>
	<htmltext><p>Physics (yes, Physics, THE hardest of hard sciences) is full of terrible mathematics, absolutely terrible, shockingly bad stuff. The good ones know it, some will say it doesn't matter because their butchery comes up with "accurate" results. If they can't even get their analysis right, what can we expect of the softer sciences? That said physics is not so much concerned with statistics as it is probability, none the less, they have some serious problems, for example they often simply decide highly non-convergent things should converge because the experiment says it should...</p><p>The greatest tragedy in modern science (in my eyes) is the loss of physics as a hard science, currently these guys are way off with the fairies and producing nothing of worth, string theorists are the worst. We'll see what the CERN guys manage to come up with, but right now the mathematicians have taken the ball and run with it. It has been said that physics has become too hard for the Physicists...</p><p>I am not trolling, I am quite serious about Physicists playing dodgey games with mathematics.</p></htmltext>
<tokenext>Physics ( yes , Physics , THE hardest of hard sciences ) is full of terrible mathematics , absolutely terrible , shockingly bad stuff .
The good ones know it , some will say it does n't matter because their butchery comes up with " accurate " results .
If they ca n't even get their analysis right , what can we expect of the softer sciences ?
That said physics is not so much concerned with statistics as it is probability , none the less , they have some serious problems , for example they often simply decide highly non-convergent things should converge because the experiment says it should...The greatest tragedy in modern science ( in my eyes ) is the loss of physics as a hard science , currently these guys are way off with the fairies and producing nothing of worth , string theorists are the worst .
We 'll see what the CERN guys manage to come up with , but right now the mathematicians have taken the ball and run with it .
It has been said that physics has become too hard for the Physicists...I am not trolling , I am quite serious about Physicists playing dodgey games with mathematics .</tokentext>
<sentencetext>Physics (yes, Physics, THE hardest of hard sciences) is full of terrible mathematics, absolutely terrible, shockingly bad stuff.
The good ones know it, some will say it doesn't matter because their butchery comes up with "accurate" results.
If they can't even get their analysis right, what can we expect of the softer sciences?
That said physics is not so much concerned with statistics as it is probability, none the less, they have some serious problems, for example they often simply decide highly non-convergent things should converge because the experiment says it should...The greatest tragedy in modern science (in my eyes) is the loss of physics as a hard science, currently these guys are way off with the fairies and producing nothing of worth, string theorists are the worst.
We'll see what the CERN guys manage to come up with, but right now the mathematicians have taken the ball and run with it.
It has been said that physics has become too hard for the Physicists...I am not trolling, I am quite serious about Physicists playing dodgey games with mathematics.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518692</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519024</id>
	<title>Re:Long winded troll</title>
	<author>glwtta</author>
	<datestamp>1268845860000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><i>So what is it in the business of?</i>
<br> <br>
Disproof.</htmltext>
<tokenext>So what is it in the business of ?
Disproof .</tokentext>
<sentencetext>So what is it in the business of?
Disproof.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518526</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519762</id>
	<title>Re:Not Scientists</title>
	<author>Secret Rabbit</author>
	<datestamp>1268943180000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>*posted as is without editing, worts and all*</p><p>There is a difference between Medicine and Biology; they are NOT the same thing.  Medicine is the biology of the human body.  Period.  End of story.  Biology concerns itself will ALL life.    In short, Medicine is the APPLICATION of Biology to humans.  Different.  But, if I'm wrong, go ahead and explain to me how those two domains are the same thing and the same size.</p><p>When it comes to Biology's contribution to Medicine, why don't you actually look up what the Engineers and Physicists have done compared to the Biologists before commenting.  Biology has really only come into play recently.</p><p>Furthermore, the more you get away from Maths, obviously, the less will be known.  However, if you've look at the modern Chemistry curriculum, and consider what needs to be known to understand the typical *required* Quantum Chemistry course... that's a fair bit of Maths.  Btw, there's a reason why I mentioned Biology's relatively limited contribution to Science.  It's because they've really only come into there own, as a Science, recently.  Another couple decades or so, and they might be where Chemistry was a couple decades ago.  Most of Chemistry today is actually quite good.</p><p>When it comes to the causation/correlation problem, yes it is a BIG problem.  Just look through PubMed if you don't believe me.  It is *very* common to have papers on there that calculate CIs with 20-30 patients (or less) like it means something.  Sorry, but if they think that, they're clueless.  It takes a statistically significant number of patients studied to make a CI meaningful.  That's why I only really pay attention to survey studies (and view others with extreme scrutiny).  They are the ones that have the highest possibility of being worth reading.</p><p>Finally, I have worked with Scientists.  Physicists in particular.  I also have payed attention to what the other disciplines have put out.  Chemistry is meh, Biology is lesser (to one degree or another depending on the specific field within it) and Medicine is a joke.  It might be politically incorrect to say such things.  But, it is the honest truth.  There's not really any shame in it as the more applied one goes, the more complicated things get.  But, to ignore ones place is inviting disaster.  That's really the point.  To get them to know there place.  Enough people have died due to there god complexes, overconfidence and not really understanding things (and not knowing it).  They really need to acknowledge the limitations of what they do and who they are.</p><p>When it comes to the MDs that I get along with and respect.  It's those that explicitly state what they are comfortable doing and what they aren't.  It's those that are willing to work<nobr> <wbr></nobr>/with/ me not the ones who think its OK to tell me what to do when it's something that I care to be involved in.  Etc.  Guess which type is more rare and the average age of the ones that are more humble.</p></htmltext>
<tokenext>* posted as is without editing , worts and all * There is a difference between Medicine and Biology ; they are NOT the same thing .
Medicine is the biology of the human body .
Period. End of story .
Biology concerns itself will ALL life .
In short , Medicine is the APPLICATION of Biology to humans .
Different. But , if I 'm wrong , go ahead and explain to me how those two domains are the same thing and the same size.When it comes to Biology 's contribution to Medicine , why do n't you actually look up what the Engineers and Physicists have done compared to the Biologists before commenting .
Biology has really only come into play recently.Furthermore , the more you get away from Maths , obviously , the less will be known .
However , if you 've look at the modern Chemistry curriculum , and consider what needs to be known to understand the typical * required * Quantum Chemistry course... that 's a fair bit of Maths .
Btw , there 's a reason why I mentioned Biology 's relatively limited contribution to Science .
It 's because they 've really only come into there own , as a Science , recently .
Another couple decades or so , and they might be where Chemistry was a couple decades ago .
Most of Chemistry today is actually quite good.When it comes to the causation/correlation problem , yes it is a BIG problem .
Just look through PubMed if you do n't believe me .
It is * very * common to have papers on there that calculate CIs with 20-30 patients ( or less ) like it means something .
Sorry , but if they think that , they 're clueless .
It takes a statistically significant number of patients studied to make a CI meaningful .
That 's why I only really pay attention to survey studies ( and view others with extreme scrutiny ) .
They are the ones that have the highest possibility of being worth reading.Finally , I have worked with Scientists .
Physicists in particular .
I also have payed attention to what the other disciplines have put out .
Chemistry is meh , Biology is lesser ( to one degree or another depending on the specific field within it ) and Medicine is a joke .
It might be politically incorrect to say such things .
But , it is the honest truth .
There 's not really any shame in it as the more applied one goes , the more complicated things get .
But , to ignore ones place is inviting disaster .
That 's really the point .
To get them to know there place .
Enough people have died due to there god complexes , overconfidence and not really understanding things ( and not knowing it ) .
They really need to acknowledge the limitations of what they do and who they are.When it comes to the MDs that I get along with and respect .
It 's those that explicitly state what they are comfortable doing and what they are n't .
It 's those that are willing to work /with/ me not the ones who think its OK to tell me what to do when it 's something that I care to be involved in .
Etc. Guess which type is more rare and the average age of the ones that are more humble .</tokentext>
<sentencetext>*posted as is without editing, worts and all*There is a difference between Medicine and Biology; they are NOT the same thing.
Medicine is the biology of the human body.
Period.  End of story.
Biology concerns itself will ALL life.
In short, Medicine is the APPLICATION of Biology to humans.
Different.  But, if I'm wrong, go ahead and explain to me how those two domains are the same thing and the same size.When it comes to Biology's contribution to Medicine, why don't you actually look up what the Engineers and Physicists have done compared to the Biologists before commenting.
Biology has really only come into play recently.Furthermore, the more you get away from Maths, obviously, the less will be known.
However, if you've look at the modern Chemistry curriculum, and consider what needs to be known to understand the typical *required* Quantum Chemistry course... that's a fair bit of Maths.
Btw, there's a reason why I mentioned Biology's relatively limited contribution to Science.
It's because they've really only come into there own, as a Science, recently.
Another couple decades or so, and they might be where Chemistry was a couple decades ago.
Most of Chemistry today is actually quite good.When it comes to the causation/correlation problem, yes it is a BIG problem.
Just look through PubMed if you don't believe me.
It is *very* common to have papers on there that calculate CIs with 20-30 patients (or less) like it means something.
Sorry, but if they think that, they're clueless.
It takes a statistically significant number of patients studied to make a CI meaningful.
That's why I only really pay attention to survey studies (and view others with extreme scrutiny).
They are the ones that have the highest possibility of being worth reading.Finally, I have worked with Scientists.
Physicists in particular.
I also have payed attention to what the other disciplines have put out.
Chemistry is meh, Biology is lesser (to one degree or another depending on the specific field within it) and Medicine is a joke.
It might be politically incorrect to say such things.
But, it is the honest truth.
There's not really any shame in it as the more applied one goes, the more complicated things get.
But, to ignore ones place is inviting disaster.
That's really the point.
To get them to know there place.
Enough people have died due to there god complexes, overconfidence and not really understanding things (and not knowing it).
They really need to acknowledge the limitations of what they do and who they are.When it comes to the MDs that I get along with and respect.
It's those that explicitly state what they are comfortable doing and what they aren't.
It's those that are willing to work /with/ me not the ones who think its OK to tell me what to do when it's something that I care to be involved in.
Etc.  Guess which type is more rare and the average age of the ones that are more humble.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518958</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31522076</id>
	<title>Re:Looking for a good book on statistics</title>
	<author>Chemisor</author>
	<datestamp>1268923620000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><a href="http://www.amazon.com/Probability-Theory-Logic-Science-Vol/dp/0521592712" title="amazon.com">Probability: The Logic Of Science</a> [amazon.com] by Jaynes. Although it is in part a rigorous text, you can skip the derivations and just read the examples. Most of the book is about how to think about probability, emphasizing the methods of correctly formulating the problem and explaining why most people fail at that (admittedly quite complex) task. Even if you don't understand a single equation in the book, you'll still benefit from reading it.</p></htmltext>
<tokenext>Probability : The Logic Of Science [ amazon.com ] by Jaynes .
Although it is in part a rigorous text , you can skip the derivations and just read the examples .
Most of the book is about how to think about probability , emphasizing the methods of correctly formulating the problem and explaining why most people fail at that ( admittedly quite complex ) task .
Even if you do n't understand a single equation in the book , you 'll still benefit from reading it .</tokentext>
<sentencetext>Probability: The Logic Of Science [amazon.com] by Jaynes.
Although it is in part a rigorous text, you can skip the derivations and just read the examples.
Most of the book is about how to think about probability, emphasizing the methods of correctly formulating the problem and explaining why most people fail at that (admittedly quite complex) task.
Even if you don't understand a single equation in the book, you'll still benefit from reading it.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519618</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31522782</id>
	<title>Re:Personal experience</title>
	<author>Anonymous</author>
	<datestamp>1268926920000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>As someone, who has to study a lot of statistics:  My first and persistent impression of that field is futility.</p><p>Basic literature has about 1000 pages packed and crammed with math; highly compressed information with literally hundreds of special cases and restrictions and tiny interpretational tolerance. You don't only have to know complicated (not necessary difficult) algorithms, you have to know when and why they apply and how to interpret the results under certain circumstances. And that's only standard procedures, implying that your data- sets and experiments conform to text-book examples. If they don't your're already partially fucked, and have to hope that some statistics guru published something that maybe works. What's more, you almost never know for sure, whether you did it right since there are often many different methods (and sometimes different versions of a single method)  that ALL work (as they don't produce errors or unreasonable results) but there's only the one method which results meet the required criteria. Those criteria are not fixed, though, they are reasonable assumptions, made by smart people, but are just adjusted towards their impression of a good balance of the many different factors that influence validity. There are conflicting opinions. Then you sometimes have the needs to combine different algorithms, and that's when you really leave known grounds (at high speed). Things then get so complicated you can't reasonably interpret your results, even if you find someone who has published something about that, as it's highly probable he missed something. To be certain you really need to know the underlying math from the ground up, which is something you've not been tought, probably, if you're not a mathematican, as it's way to complex and difficult usually. Even if you know that, you can fail at so many different levels just by misinterpreting the interpretational consequences that some factor has on subsequent calculations, without invalidating the algebraic correctness of your arithmetics.</p><p>It's so complex, you can't reasonably assume you have grasped every potential mistake, therefore, there's a certain probability that you're wrong. Yet there's no effective factor like that, that is influencing the expressed validity of results.<br>The only solution I see (for average brained scientists) is, to keep it clear and simple, but reality can't always be looked at that way.</p></htmltext>
<tokenext>As someone , who has to study a lot of statistics : My first and persistent impression of that field is futility.Basic literature has about 1000 pages packed and crammed with math ; highly compressed information with literally hundreds of special cases and restrictions and tiny interpretational tolerance .
You do n't only have to know complicated ( not necessary difficult ) algorithms , you have to know when and why they apply and how to interpret the results under certain circumstances .
And that 's only standard procedures , implying that your data- sets and experiments conform to text-book examples .
If they do n't your 're already partially fucked , and have to hope that some statistics guru published something that maybe works .
What 's more , you almost never know for sure , whether you did it right since there are often many different methods ( and sometimes different versions of a single method ) that ALL work ( as they do n't produce errors or unreasonable results ) but there 's only the one method which results meet the required criteria .
Those criteria are not fixed , though , they are reasonable assumptions , made by smart people , but are just adjusted towards their impression of a good balance of the many different factors that influence validity .
There are conflicting opinions .
Then you sometimes have the needs to combine different algorithms , and that 's when you really leave known grounds ( at high speed ) .
Things then get so complicated you ca n't reasonably interpret your results , even if you find someone who has published something about that , as it 's highly probable he missed something .
To be certain you really need to know the underlying math from the ground up , which is something you 've not been tought , probably , if you 're not a mathematican , as it 's way to complex and difficult usually .
Even if you know that , you can fail at so many different levels just by misinterpreting the interpretational consequences that some factor has on subsequent calculations , without invalidating the algebraic correctness of your arithmetics.It 's so complex , you ca n't reasonably assume you have grasped every potential mistake , therefore , there 's a certain probability that you 're wrong .
Yet there 's no effective factor like that , that is influencing the expressed validity of results.The only solution I see ( for average brained scientists ) is , to keep it clear and simple , but reality ca n't always be looked at that way .</tokentext>
<sentencetext>As someone, who has to study a lot of statistics:  My first and persistent impression of that field is futility.Basic literature has about 1000 pages packed and crammed with math; highly compressed information with literally hundreds of special cases and restrictions and tiny interpretational tolerance.
You don't only have to know complicated (not necessary difficult) algorithms, you have to know when and why they apply and how to interpret the results under certain circumstances.
And that's only standard procedures, implying that your data- sets and experiments conform to text-book examples.
If they don't your're already partially fucked, and have to hope that some statistics guru published something that maybe works.
What's more, you almost never know for sure, whether you did it right since there are often many different methods (and sometimes different versions of a single method)  that ALL work (as they don't produce errors or unreasonable results) but there's only the one method which results meet the required criteria.
Those criteria are not fixed, though, they are reasonable assumptions, made by smart people, but are just adjusted towards their impression of a good balance of the many different factors that influence validity.
There are conflicting opinions.
Then you sometimes have the needs to combine different algorithms, and that's when you really leave known grounds (at high speed).
Things then get so complicated you can't reasonably interpret your results, even if you find someone who has published something about that, as it's highly probable he missed something.
To be certain you really need to know the underlying math from the ground up, which is something you've not been tought, probably, if you're not a mathematican, as it's way to complex and difficult usually.
Even if you know that, you can fail at so many different levels just by misinterpreting the interpretational consequences that some factor has on subsequent calculations, without invalidating the algebraic correctness of your arithmetics.It's so complex, you can't reasonably assume you have grasped every potential mistake, therefore, there's a certain probability that you're wrong.
Yet there's no effective factor like that, that is influencing the expressed validity of results.The only solution I see (for average brained scientists) is, to keep it clear and simple, but reality can't always be looked at that way.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518718</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520310</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Ihlosi</author>
	<datestamp>1268908500000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><i>That's not what people become shrinks for, though. They want to sit in their office, put people on their couch (or, more modern, in a comfy chair) and get 100 bucks an hour for listening to some idiot whine. And most do just that and will do fine.</i> </p><p>You're talking about psychologists, not psychiatrists. Psychologists are the ones who are highly paid for listening to you, psychiatrists will fill you up with risperdal, prozac, ritalin or whatever.</p></htmltext>
<tokenext>That 's not what people become shrinks for , though .
They want to sit in their office , put people on their couch ( or , more modern , in a comfy chair ) and get 100 bucks an hour for listening to some idiot whine .
And most do just that and will do fine .
You 're talking about psychologists , not psychiatrists .
Psychologists are the ones who are highly paid for listening to you , psychiatrists will fill you up with risperdal , prozac , ritalin or whatever .</tokentext>
<sentencetext>That's not what people become shrinks for, though.
They want to sit in their office, put people on their couch (or, more modern, in a comfy chair) and get 100 bucks an hour for listening to some idiot whine.
And most do just that and will do fine.
You're talking about psychologists, not psychiatrists.
Psychologists are the ones who are highly paid for listening to you, psychiatrists will fill you up with risperdal, prozac, ritalin or whatever.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518766</id>
	<title>Re:Example: Standard Deviation</title>
	<author>PSUspud</author>
	<datestamp>1268843520000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>2</modscore>
	<htmltext><p>As a statistics teacher (HS / Tech school level), this doesn't surprise me in the least.  Statistics and statistics education has become a giant game of "plug the numbers in and damn the understanding".  When a student has never calculated a standard deviation by hand, how can they be expected to know what the heck a root mean square deviation from the sample mean really is?</p><p>Going further, I would say that statistics is a tool for answering questions.  Like any other tool, it works well for some jobs and not for others.  So far, no problem.  But the problem comes from students that are just not willing to understand the questions that statistics can answer.  Case in point -- a p value of 0.05 does \_not\_ mean that the null hypothesis has a 95\% chance of being wrong.  That's what stats students want it to mean, because they are not willing to ask the questions that stats can answer.</p><p>Until students are willing to actually do the work, for the sake of actually learning, I don't see any hope.</p></htmltext>
<tokenext>As a statistics teacher ( HS / Tech school level ) , this does n't surprise me in the least .
Statistics and statistics education has become a giant game of " plug the numbers in and damn the understanding " .
When a student has never calculated a standard deviation by hand , how can they be expected to know what the heck a root mean square deviation from the sample mean really is ? Going further , I would say that statistics is a tool for answering questions .
Like any other tool , it works well for some jobs and not for others .
So far , no problem .
But the problem comes from students that are just not willing to understand the questions that statistics can answer .
Case in point -- a p value of 0.05 does \ _not \ _ mean that the null hypothesis has a 95 \ % chance of being wrong .
That 's what stats students want it to mean , because they are not willing to ask the questions that stats can answer.Until students are willing to actually do the work , for the sake of actually learning , I do n't see any hope .</tokentext>
<sentencetext>As a statistics teacher (HS / Tech school level), this doesn't surprise me in the least.
Statistics and statistics education has become a giant game of "plug the numbers in and damn the understanding".
When a student has never calculated a standard deviation by hand, how can they be expected to know what the heck a root mean square deviation from the sample mean really is?Going further, I would say that statistics is a tool for answering questions.
Like any other tool, it works well for some jobs and not for others.
So far, no problem.
But the problem comes from students that are just not willing to understand the questions that statistics can answer.
Case in point -- a p value of 0.05 does \_not\_ mean that the null hypothesis has a 95\% chance of being wrong.
That's what stats students want it to mean, because they are not willing to ask the questions that stats can answer.Until students are willing to actually do the work, for the sake of actually learning, I don't see any hope.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518402</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518394</id>
	<title>Summery?</title>
	<author>sincewhen</author>
	<datestamp>1268840400000</datestamp>
	<modclass>Funny</modclass>
	<modscore>4</modscore>
	<htmltext><p>It's not just statistics that people have a problem with...</p></htmltext>
<tokenext>It 's not just statistics that people have a problem with.. .</tokentext>
<sentencetext>It's not just statistics that people have a problem with...</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520674</id>
	<title>Data, data...</title>
	<author>Anonymous</author>
	<datestamp>1268913180000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>There are three kinds of lies: harmless lies, harmful lies...and then there's statistics<nobr> <wbr></nobr>;)</p></htmltext>
<tokenext>There are three kinds of lies : harmless lies , harmful lies...and then there 's statistics ; )</tokentext>
<sentencetext>There are three kinds of lies: harmless lies, harmful lies...and then there's statistics ;)</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519624</id>
	<title>Re:The problem is statisticians</title>
	<author>the\_womble</author>
	<datestamp>1268854680000</datestamp>
	<modclass>Funny</modclass>
	<modscore>3</modscore>
	<htmltext><p>I feel somewhat vindicated for being no good at econometrics when I see where the people who were good at it have landed us.....</p></htmltext>
<tokenext>I feel somewhat vindicated for being no good at econometrics when I see where the people who were good at it have landed us.... .</tokentext>
<sentencetext>I feel somewhat vindicated for being no good at econometrics when I see where the people who were good at it have landed us.....</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518544</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31524222</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268933880000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>True. You ask a Doctor if 1 in a 1000 people have a disease and a 95\% accurate test flags you as one who does what is the chance that you have the disease?</p><p>Remember these are the people who campaigned for mammograms despite the fact that they pick up only 8 of 12 while giving 50 false positives.</p></htmltext>
<tokenext>True .
You ask a Doctor if 1 in a 1000 people have a disease and a 95 \ % accurate test flags you as one who does what is the chance that you have the disease ? Remember these are the people who campaigned for mammograms despite the fact that they pick up only 8 of 12 while giving 50 false positives .</tokentext>
<sentencetext>True.
You ask a Doctor if 1 in a 1000 people have a disease and a 95\% accurate test flags you as one who does what is the chance that you have the disease?Remember these are the people who campaigned for mammograms despite the fact that they pick up only 8 of 12 while giving 50 false positives.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31533692</id>
	<title>Anonymous Coward</title>
	<author>Anonymous</author>
	<datestamp>1269031380000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>When I started teaching I was telling what is in the book. he basic problem of statistics is that we are using ratio analysis which is subjected to error. Statistics is similar to Geometry where you hypothesize if two triangles are congruent or not. Both statistics and Geometry use inductive logic(or thinking), thus are not similar to arithmetic.algebra or calculus. Unfortunately we do not teach statistics as an interesting tool for investigation of  non-algebraic system like human behavior, disease behavior etc. I am in the process of getting a patent on my statistics methods and teaching material. I have tested this with over 300 graduate students in blind study with about 85\% understand and use the knowledge in their field. Blaming doctors is not right though most hear such statistics from sales people (of medical/ pharmaceutical companies). In general, statistics controls our life and those who don't understand or use become part of the statistics. So, who teaches statistics and how it is taught finally determines the usefulness or misuse of statistics and it is not the fault of the subject itself. When the population being sample is stratified, that is not homogeneous and if all the associated facts are not carefully selected, then statistics tell lies. We get only 40\% real information in any situation and about 60\% have to be carefully collected or assumed. If the assumptions are wrong and when we collect wrong data, every thing fails. Take for example, brilliant mathematicians and engineers working for the Banks etc., did not take the human behavior of consumers in US, the statistics failed!.</p></htmltext>
<tokenext>When I started teaching I was telling what is in the book .
he basic problem of statistics is that we are using ratio analysis which is subjected to error .
Statistics is similar to Geometry where you hypothesize if two triangles are congruent or not .
Both statistics and Geometry use inductive logic ( or thinking ) , thus are not similar to arithmetic.algebra or calculus .
Unfortunately we do not teach statistics as an interesting tool for investigation of non-algebraic system like human behavior , disease behavior etc .
I am in the process of getting a patent on my statistics methods and teaching material .
I have tested this with over 300 graduate students in blind study with about 85 \ % understand and use the knowledge in their field .
Blaming doctors is not right though most hear such statistics from sales people ( of medical/ pharmaceutical companies ) .
In general , statistics controls our life and those who do n't understand or use become part of the statistics .
So , who teaches statistics and how it is taught finally determines the usefulness or misuse of statistics and it is not the fault of the subject itself .
When the population being sample is stratified , that is not homogeneous and if all the associated facts are not carefully selected , then statistics tell lies .
We get only 40 \ % real information in any situation and about 60 \ % have to be carefully collected or assumed .
If the assumptions are wrong and when we collect wrong data , every thing fails .
Take for example , brilliant mathematicians and engineers working for the Banks etc. , did not take the human behavior of consumers in US , the statistics failed ! .</tokentext>
<sentencetext>When I started teaching I was telling what is in the book.
he basic problem of statistics is that we are using ratio analysis which is subjected to error.
Statistics is similar to Geometry where you hypothesize if two triangles are congruent or not.
Both statistics and Geometry use inductive logic(or thinking), thus are not similar to arithmetic.algebra or calculus.
Unfortunately we do not teach statistics as an interesting tool for investigation of  non-algebraic system like human behavior, disease behavior etc.
I am in the process of getting a patent on my statistics methods and teaching material.
I have tested this with over 300 graduate students in blind study with about 85\% understand and use the knowledge in their field.
Blaming doctors is not right though most hear such statistics from sales people (of medical/ pharmaceutical companies).
In general, statistics controls our life and those who don't understand or use become part of the statistics.
So, who teaches statistics and how it is taught finally determines the usefulness or misuse of statistics and it is not the fault of the subject itself.
When the population being sample is stratified, that is not homogeneous and if all the associated facts are not carefully selected, then statistics tell lies.
We get only 40\% real information in any situation and about 60\% have to be carefully collected or assumed.
If the assumptions are wrong and when we collect wrong data, every thing fails.
Take for example, brilliant mathematicians and engineers working for the Banks etc., did not take the human behavior of consumers in US, the statistics failed!.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518484</id>
	<title>Personal experience</title>
	<author>Anonymous</author>
	<datestamp>1268841180000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>5</modscore>
	<htmltext><p>As a doctor myself, I feel I should add my $0.02...</p><p>Throughout med school we had the odd scattered lecture on statistics, and later when reading papers I used to skim over most of the maths just to look for the P value at the end (one representation of how statistically significant a result is).</p><p>However, I then took a formal stats course and was amazed at how little I understood - Monte Carlo techniques, Markov models, and even something as trivial yet important as the difference between a parametric versus a non-parametric test.</p><p>And then it struck me - most of the research I had read had applied parametric statistical tests to their data - that it, the researchers made an assumption that the underlying distribution of results would fall on a normal curve. Yet this simple assumption may be all it takes to skew the data when they should have chosen a non-parametric test instead.</p><p>So yes, stats are vitally important, badly taught, and focus too much on the maths rather than the concepts. Remember that we're doctors, not mathematicians - the last set of sums I did were in high school. If I need to analyse data, I'll probably plug it into SPSS - although now with my eyes open.</p><p>-Nano.</p></htmltext>
<tokenext>As a doctor myself , I feel I should add my $ 0.02...Throughout med school we had the odd scattered lecture on statistics , and later when reading papers I used to skim over most of the maths just to look for the P value at the end ( one representation of how statistically significant a result is ) .However , I then took a formal stats course and was amazed at how little I understood - Monte Carlo techniques , Markov models , and even something as trivial yet important as the difference between a parametric versus a non-parametric test.And then it struck me - most of the research I had read had applied parametric statistical tests to their data - that it , the researchers made an assumption that the underlying distribution of results would fall on a normal curve .
Yet this simple assumption may be all it takes to skew the data when they should have chosen a non-parametric test instead.So yes , stats are vitally important , badly taught , and focus too much on the maths rather than the concepts .
Remember that we 're doctors , not mathematicians - the last set of sums I did were in high school .
If I need to analyse data , I 'll probably plug it into SPSS - although now with my eyes open.-Nano .</tokentext>
<sentencetext>As a doctor myself, I feel I should add my $0.02...Throughout med school we had the odd scattered lecture on statistics, and later when reading papers I used to skim over most of the maths just to look for the P value at the end (one representation of how statistically significant a result is).However, I then took a formal stats course and was amazed at how little I understood - Monte Carlo techniques, Markov models, and even something as trivial yet important as the difference between a parametric versus a non-parametric test.And then it struck me - most of the research I had read had applied parametric statistical tests to their data - that it, the researchers made an assumption that the underlying distribution of results would fall on a normal curve.
Yet this simple assumption may be all it takes to skew the data when they should have chosen a non-parametric test instead.So yes, stats are vitally important, badly taught, and focus too much on the maths rather than the concepts.
Remember that we're doctors, not mathematicians - the last set of sums I did were in high school.
If I need to analyse data, I'll probably plug it into SPSS - although now with my eyes open.-Nano.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31524370</id>
	<title>Re:Looking for a good book on statistics</title>
	<author>Anonymous</author>
	<datestamp>1268934420000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>I've done only an introduction to statistics class but did a lot of math in my engineering classes.  I'm currently reading this book because I'd like to setup my own tests</p><p>http://books.google.com/books?id=6HkwH7kJD0oC&amp;printsec=frontcover&amp;dq=intelligent+data+analysis&amp;source=bl&amp;ots=cMv8jmV2b2&amp;sig=EXHZHiCrBpuWTIOaHtTvOpDbiF8&amp;hl=en&amp;ei=-FeiS9TLCsWclgetk8yUCQ&amp;sa=X&amp;oi=book\_result&amp;ct=result&amp;resnum=4&amp;ved=0CB4Q6AEwAw#v=onepage&amp;q=&amp;f=false</p><p>There is very little math and derivations, but it does touch upon why different things are useful and when they're not appropriate.</p></htmltext>
<tokenext>I 've done only an introduction to statistics class but did a lot of math in my engineering classes .
I 'm currently reading this book because I 'd like to setup my own testshttp : //books.google.com/books ? id = 6HkwH7kJD0oC&amp;printsec = frontcover&amp;dq = intelligent + data + analysis&amp;source = bl&amp;ots = cMv8jmV2b2&amp;sig = EXHZHiCrBpuWTIOaHtTvOpDbiF8&amp;hl = en&amp;ei = -FeiS9TLCsWclgetk8yUCQ&amp;sa = X&amp;oi = book \ _result&amp;ct = result&amp;resnum = 4&amp;ved = 0CB4Q6AEwAw # v = onepage&amp;q = &amp;f = falseThere is very little math and derivations , but it does touch upon why different things are useful and when they 're not appropriate .</tokentext>
<sentencetext>I've done only an introduction to statistics class but did a lot of math in my engineering classes.
I'm currently reading this book because I'd like to setup my own testshttp://books.google.com/books?id=6HkwH7kJD0oC&amp;printsec=frontcover&amp;dq=intelligent+data+analysis&amp;source=bl&amp;ots=cMv8jmV2b2&amp;sig=EXHZHiCrBpuWTIOaHtTvOpDbiF8&amp;hl=en&amp;ei=-FeiS9TLCsWclgetk8yUCQ&amp;sa=X&amp;oi=book\_result&amp;ct=result&amp;resnum=4&amp;ved=0CB4Q6AEwAw#v=onepage&amp;q=&amp;f=falseThere is very little math and derivations, but it does touch upon why different things are useful and when they're not appropriate.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519618</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519542</id>
	<title>Re:Lies, Damned Lies, and Statistics.</title>
	<author>DynaSoar</author>
	<datestamp>1268853180000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>there are statistically two popes per square kilometer in the vatican.</p></div><p>One does not do statistics on single data points. Statistics are for estimating results in large numbers of cases using smaller numbers of cases. When there is only one case, we use an entirely different means of reporting data. It is called a measurement. There is one vatican. Results would be reported as X per vatican. By direct measure, the answer is one pope per vatican. Your own result reflects your incorrect thinking. There is no square kilometer in one vatican. One does not use a unit of measure of area for a target smaller than that unit.</p></div>
	</htmltext>
<tokenext>there are statistically two popes per square kilometer in the vatican.One does not do statistics on single data points .
Statistics are for estimating results in large numbers of cases using smaller numbers of cases .
When there is only one case , we use an entirely different means of reporting data .
It is called a measurement .
There is one vatican .
Results would be reported as X per vatican .
By direct measure , the answer is one pope per vatican .
Your own result reflects your incorrect thinking .
There is no square kilometer in one vatican .
One does not use a unit of measure of area for a target smaller than that unit .</tokentext>
<sentencetext>there are statistically two popes per square kilometer in the vatican.One does not do statistics on single data points.
Statistics are for estimating results in large numbers of cases using smaller numbers of cases.
When there is only one case, we use an entirely different means of reporting data.
It is called a measurement.
There is one vatican.
Results would be reported as X per vatican.
By direct measure, the answer is one pope per vatican.
Your own result reflects your incorrect thinking.
There is no square kilometer in one vatican.
One does not use a unit of measure of area for a target smaller than that unit.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518344</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520012</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268904120000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p><nobr> <wbr></nobr></p><div class="quote"><p>...9 out of 10 that somehow managed to get their diploma by either learning what they absolutely needed (and forgot it right after the test[...]) or by cribbing altogether.</p></div><p>I don't believe your statistic!</p></div>
	</htmltext>
<tokenext>...9 out of 10 that somehow managed to get their diploma by either learning what they absolutely needed ( and forgot it right after the test [ ... ] ) or by cribbing altogether.I do n't believe your statistic !</tokentext>
<sentencetext> ...9 out of 10 that somehow managed to get their diploma by either learning what they absolutely needed (and forgot it right after the test[...]) or by cribbing altogether.I don't believe your statistic!
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518796</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268843700000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext>Why would you care to test him? You sound like a very unpleasant person to deal with.</htmltext>
<tokenext>Why would you care to test him ?
You sound like a very unpleasant person to deal with .</tokentext>
<sentencetext>Why would you care to test him?
You sound like a very unpleasant person to deal with.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518402</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518600</id>
	<title>Re:Personal experience</title>
	<author>Anonymous</author>
	<datestamp>1268842200000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Well one thing to consider is the stigma in the honors/college prep programs in HS where statistics is looked at as the "Math for dumb kids" where the "brighter" students take calculus and the like.</p></htmltext>
<tokenext>Well one thing to consider is the stigma in the honors/college prep programs in HS where statistics is looked at as the " Math for dumb kids " where the " brighter " students take calculus and the like .</tokentext>
<sentencetext>Well one thing to consider is the stigma in the honors/college prep programs in HS where statistics is looked at as the "Math for dumb kids" where the "brighter" students take calculus and the like.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518484</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518680</id>
	<title>Re:Personal experience</title>
	<author>spasm</author>
	<datestamp>1268842680000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>And that, my friend, is why the NIH's constant push to produce more 'physician-researchers' continues to drive me nuts.  Because they rarely insist K awards and other early-career training mechanisms require physicians intending to do research in areas where stats are important actually get any stats training..</p></htmltext>
<tokenext>And that , my friend , is why the NIH 's constant push to produce more 'physician-researchers ' continues to drive me nuts .
Because they rarely insist K awards and other early-career training mechanisms require physicians intending to do research in areas where stats are important actually get any stats training. .</tokentext>
<sentencetext>And that, my friend, is why the NIH's constant push to produce more 'physician-researchers' continues to drive me nuts.
Because they rarely insist K awards and other early-career training mechanisms require physicians intending to do research in areas where stats are important actually get any stats training..</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518484</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520102</id>
	<title>Only if you restrict it to live ones...</title>
	<author>Anonymous</author>
	<datestamp>1268905320000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>&gt; there are statistically two popes per square kilometer in the vatican.</p><p>Two LIVE popes, you mean.  You'll find many more per square kilometer if you remove that restriction.</p><p>Which means we should be grateful that there haven't been more anti-popes, given their estimated mass, close proximity and E = m*c^2<nobr> <wbr></nobr>...</p></htmltext>
<tokenext>&gt; there are statistically two popes per square kilometer in the vatican.Two LIVE popes , you mean .
You 'll find many more per square kilometer if you remove that restriction.Which means we should be grateful that there have n't been more anti-popes , given their estimated mass , close proximity and E = m * c ^ 2 .. .</tokentext>
<sentencetext>&gt; there are statistically two popes per square kilometer in the vatican.Two LIVE popes, you mean.
You'll find many more per square kilometer if you remove that restriction.Which means we should be grateful that there haven't been more anti-popes, given their estimated mass, close proximity and E = m*c^2 ...</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518344</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518556</id>
	<title>Current Data</title>
	<author>Dripdry</author>
	<datestamp>1268841720000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Does that mean that we should send people who know what they're doing to sort through results and draw more meaningful conclusions? Or just rerun the tests?</p><p>This seems obvious, so please don't waste mod points here, people who know what they're actually talking about will probably chime in.</p></htmltext>
<tokenext>Does that mean that we should send people who know what they 're doing to sort through results and draw more meaningful conclusions ?
Or just rerun the tests ? This seems obvious , so please do n't waste mod points here , people who know what they 're actually talking about will probably chime in .</tokentext>
<sentencetext>Does that mean that we should send people who know what they're doing to sort through results and draw more meaningful conclusions?
Or just rerun the tests?This seems obvious, so please don't waste mod points here, people who know what they're actually talking about will probably chime in.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31528110</id>
	<title>Most scientific papers are probably wrong</title>
	<author>Anonymous</author>
	<datestamp>1268904240000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>http://www.newscientist.com/article/dn7915</p><p>we see what we want to see, we see what we are paid to see</p></htmltext>
<tokenext>http : //www.newscientist.com/article/dn7915we see what we want to see , we see what we are paid to see</tokentext>
<sentencetext>http://www.newscientist.com/article/dn7915we see what we want to see, we see what we are paid to see</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518620</id>
	<title>Stats are completely useless!!!!</title>
	<author>Anonymous</author>
	<datestamp>1268842380000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>E.g.: Study shows a cancer group of size 3000 is cured by drug A 99\% of the time.<br>1\% it fails.</p><p>30 patients are dead.  No correlation it seems at the time.</p><p>*2970* patients are saved.</p><p>20 years later, it's proven a dormant undetected/sequenced gene is responsible for the 1\% failure of the drug, making it ineffective.</p><p>Statistics allowed the drug to be approved at the time that saved millions of lives.</p><p>I hate stats as much as 70\% of the average Joe<nobr> <wbr></nobr>:), but anyone with an education knows its importance.  (Esp. those dudes that are breathing right now because that drug saved their lives)</p><p>So the article in short, don't lie about your stats(or in general don't lie!) and you can benefit humanity.</p></htmltext>
<tokenext>E.g .
: Study shows a cancer group of size 3000 is cured by drug A 99 \ % of the time.1 \ % it fails.30 patients are dead .
No correlation it seems at the time .
* 2970 * patients are saved.20 years later , it 's proven a dormant undetected/sequenced gene is responsible for the 1 \ % failure of the drug , making it ineffective.Statistics allowed the drug to be approved at the time that saved millions of lives.I hate stats as much as 70 \ % of the average Joe : ) , but anyone with an education knows its importance .
( Esp. those dudes that are breathing right now because that drug saved their lives ) So the article in short , do n't lie about your stats ( or in general do n't lie !
) and you can benefit humanity .</tokentext>
<sentencetext>E.g.
: Study shows a cancer group of size 3000 is cured by drug A 99\% of the time.1\% it fails.30 patients are dead.
No correlation it seems at the time.
*2970* patients are saved.20 years later, it's proven a dormant undetected/sequenced gene is responsible for the 1\% failure of the drug, making it ineffective.Statistics allowed the drug to be approved at the time that saved millions of lives.I hate stats as much as 70\% of the average Joe :), but anyone with an education knows its importance.
(Esp. those dudes that are breathing right now because that drug saved their lives)So the article in short, don't lie about your stats(or in general don't lie!
) and you can benefit humanity.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31523702</id>
	<title>Statistics.</title>
	<author>Anonymous</author>
	<datestamp>1268931420000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p> Lies</p><p>Damn Lies</p><p> <b>Statistics</b></p><p>'nuff said......</p></htmltext>
<tokenext>LiesDamn Lies Statistics'nuff said..... .</tokentext>
<sentencetext> LiesDamn Lies Statistics'nuff said......</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519332</id>
	<title>Re:only in medicine</title>
	<author>physicsphairy</author>
	<datestamp>1268849700000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Looking at my school's course catalog, I'm not sure where people in any of the hard science programs would be getting this kind of knowledge.  Even the probability course--which students grumble about if they have to take instead of the "easier" statistics class--is not that in depth.  There are too many areas to cover in a single semester to focus on ironing out all the "gotchas" rather than simply introducing the core material.  And no statistics related courses are required beyond that one class.

</p><p>In other courses professors are rather more intent on teaching their own material.  They'll teach t-tests, etc., if they have to, but not at the depth which would address the problems described in the article.  In my experience the physics department has been the only place where they don't play fast-and-loose with mathematics <em>in general</em>.

</p><p>I think you probably have less problems in the published research of the hard sciences in that that is often interested in applying an actual theoretical model.  Misapprehending the finer points of the statistics involved is not as relevant as it is when you are just datamining the genome for any correlation you can grab at.</p></htmltext>
<tokenext>Looking at my school 's course catalog , I 'm not sure where people in any of the hard science programs would be getting this kind of knowledge .
Even the probability course--which students grumble about if they have to take instead of the " easier " statistics class--is not that in depth .
There are too many areas to cover in a single semester to focus on ironing out all the " gotchas " rather than simply introducing the core material .
And no statistics related courses are required beyond that one class .
In other courses professors are rather more intent on teaching their own material .
They 'll teach t-tests , etc. , if they have to , but not at the depth which would address the problems described in the article .
In my experience the physics department has been the only place where they do n't play fast-and-loose with mathematics in general .
I think you probably have less problems in the published research of the hard sciences in that that is often interested in applying an actual theoretical model .
Misapprehending the finer points of the statistics involved is not as relevant as it is when you are just datamining the genome for any correlation you can grab at .</tokentext>
<sentencetext>Looking at my school's course catalog, I'm not sure where people in any of the hard science programs would be getting this kind of knowledge.
Even the probability course--which students grumble about if they have to take instead of the "easier" statistics class--is not that in depth.
There are too many areas to cover in a single semester to focus on ironing out all the "gotchas" rather than simply introducing the core material.
And no statistics related courses are required beyond that one class.
In other courses professors are rather more intent on teaching their own material.
They'll teach t-tests, etc., if they have to, but not at the depth which would address the problems described in the article.
In my experience the physics department has been the only place where they don't play fast-and-loose with mathematics in general.
I think you probably have less problems in the published research of the hard sciences in that that is often interested in applying an actual theoretical model.
Misapprehending the finer points of the statistics involved is not as relevant as it is when you are just datamining the genome for any correlation you can grab at.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518692</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519016</id>
	<title>Re:Pirates cause cool weather</title>
	<author>Idiomatick</author>
	<datestamp>1268845800000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Only amusing because of the resurgence of piracy in Somalia over the last 10 years.... There has also been global cooling.</htmltext>
<tokenext>Only amusing because of the resurgence of piracy in Somalia over the last 10 years.... There has also been global cooling .</tokentext>
<sentencetext>Only amusing because of the resurgence of piracy in Somalia over the last 10 years.... There has also been global cooling.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518486</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31522776</id>
	<title>My family is full</title>
	<author>TheOutLiar</author>
	<datestamp>1268926860000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>of doctors and researchers who deal with statistics on a regular basis.

My aunt and uncle are both oncologists.  My grandfather is an orthopedist.

Last year, my grandfather discussed this very issue with me: for the majority of his career, he did not understand statistics well enough to truly gain anything from scientific journals.  He could understand things like means, standard deviation, median, etc.  But when the literature begins to lean toward more esoteric statistics, he can no longer discern the meaning.

He then handed me a book titled The Lady Tasting Tea, which he claims made a great difference in his understanding of statistics and their meanings.

I graduated with a BS in computer science, and have taken enough statistics courses that the idea of reading one more word about chi square tests would melt my brain.  But I digress.  The point is that there is accessible literature out there for people who are not versed in statistics.</htmltext>
<tokenext>of doctors and researchers who deal with statistics on a regular basis .
My aunt and uncle are both oncologists .
My grandfather is an orthopedist .
Last year , my grandfather discussed this very issue with me : for the majority of his career , he did not understand statistics well enough to truly gain anything from scientific journals .
He could understand things like means , standard deviation , median , etc .
But when the literature begins to lean toward more esoteric statistics , he can no longer discern the meaning .
He then handed me a book titled The Lady Tasting Tea , which he claims made a great difference in his understanding of statistics and their meanings .
I graduated with a BS in computer science , and have taken enough statistics courses that the idea of reading one more word about chi square tests would melt my brain .
But I digress .
The point is that there is accessible literature out there for people who are not versed in statistics .</tokentext>
<sentencetext>of doctors and researchers who deal with statistics on a regular basis.
My aunt and uncle are both oncologists.
My grandfather is an orthopedist.
Last year, my grandfather discussed this very issue with me: for the majority of his career, he did not understand statistics well enough to truly gain anything from scientific journals.
He could understand things like means, standard deviation, median, etc.
But when the literature begins to lean toward more esoteric statistics, he can no longer discern the meaning.
He then handed me a book titled The Lady Tasting Tea, which he claims made a great difference in his understanding of statistics and their meanings.
I graduated with a BS in computer science, and have taken enough statistics courses that the idea of reading one more word about chi square tests would melt my brain.
But I digress.
The point is that there is accessible literature out there for people who are not versed in statistics.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518734</id>
	<title>Bad outcomes due to statistics?</title>
	<author>scdeimos</author>
	<datestamp>1268843160000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>From TFA:</p><blockquote><div><p>&ldquo;There is increasing concern,&rdquo; declared epidemiologist John Ioannidis in a highly cited 2005 paper in PLoS Medicine, &ldquo;that in modern research, false findings may be the majority or even the vast majority of published research claims.&rdquo;</p></div></blockquote><p>One has to wonder, though: how much of that is due to misuse of statistics and how much is because it's paid research expected to get certain results in favour of those paying for the research?</p></div>
	</htmltext>
<tokenext>From TFA :    There is increasing concern ,    declared epidemiologist John Ioannidis in a highly cited 2005 paper in PLoS Medicine ,    that in modern research , false findings may be the majority or even the vast majority of published research claims.    One has to wonder , though : how much of that is due to misuse of statistics and how much is because it 's paid research expected to get certain results in favour of those paying for the research ?</tokentext>
<sentencetext>From TFA:“There is increasing concern,” declared epidemiologist John Ioannidis in a highly cited 2005 paper in PLoS Medicine, “that in modern research, false findings may be the majority or even the vast majority of published research claims.”One has to wonder, though: how much of that is due to misuse of statistics and how much is because it's paid research expected to get certain results in favour of those paying for the research?
	</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31522956</id>
	<title>Oh, yeah...</title>
	<author>russotto</author>
	<datestamp>1268927640000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Lots of statistical problems seem to be ignored.  Papers which blithely present meta-analyses as if they had the power of a single large study.  Far too much significance attributed to case-control studies (which magnify small effects and can't, by nature, show causation).  And statistical tests which simply don't have the power to show what they purport to be showing.</p><p>One example:  A study purporting to demonstrate the effect of an event E on a particular variable X.  The study took the average of the variable 12 months prior to E (high), and 12 months following E (much lower), and determined that event E reduced variable X.  Only problem is that variable X had been declining, and about the time event E happened, that decline reversed and X started going up, though more slowly than it had been declining.</p></htmltext>
<tokenext>Lots of statistical problems seem to be ignored .
Papers which blithely present meta-analyses as if they had the power of a single large study .
Far too much significance attributed to case-control studies ( which magnify small effects and ca n't , by nature , show causation ) .
And statistical tests which simply do n't have the power to show what they purport to be showing.One example : A study purporting to demonstrate the effect of an event E on a particular variable X. The study took the average of the variable 12 months prior to E ( high ) , and 12 months following E ( much lower ) , and determined that event E reduced variable X. Only problem is that variable X had been declining , and about the time event E happened , that decline reversed and X started going up , though more slowly than it had been declining .</tokentext>
<sentencetext>Lots of statistical problems seem to be ignored.
Papers which blithely present meta-analyses as if they had the power of a single large study.
Far too much significance attributed to case-control studies (which magnify small effects and can't, by nature, show causation).
And statistical tests which simply don't have the power to show what they purport to be showing.One example:  A study purporting to demonstrate the effect of an event E on a particular variable X.  The study took the average of the variable 12 months prior to E (high), and 12 months following E (much lower), and determined that event E reduced variable X.  Only problem is that variable X had been declining, and about the time event E happened, that decline reversed and X started going up, though more slowly than it had been declining.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520746</id>
	<title>Re:The problem is statisticians</title>
	<author>umghhh</author>
	<datestamp>1268914080000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>which just proves that scientists especially the 'social' brand are not real scientists. What a surprise.</htmltext>
<tokenext>which just proves that scientists especially the 'social ' brand are not real scientists .
What a surprise .</tokentext>
<sentencetext>which just proves that scientists especially the 'social' brand are not real scientists.
What a surprise.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518544</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519952</id>
	<title>My two cents</title>
	<author>identity0</author>
	<datestamp>1268903220000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>I've always thought teaching a good understanding of statistics should be a requirement for high schools, since statistics are so often (mis)used to justify public policies and legislation. We need a citizenry that can see through the bullshit, or at least think a bit critically on the subject.</p><p>I think a firm understanding of statistics is more useful than the entry level calculus and the entry-level science courses like chemistry and biology(not that those aren't good too, just not as relevant to citizenship).</p><p>Here's a nice book on statistics called "How To Lie With Statistics" that covers a lot of the ways statistics are misused. (not a referrer link or anything like that)</p><p><a href="http://www.amazon.com/How-Lie-Statistics-Darrell-Huff/dp/0393310728" title="amazon.com">http://www.amazon.com/How-Lie-Statistics-Darrell-Huff/dp/0393310728</a> [amazon.com]</p></htmltext>
<tokenext>I 've always thought teaching a good understanding of statistics should be a requirement for high schools , since statistics are so often ( mis ) used to justify public policies and legislation .
We need a citizenry that can see through the bullshit , or at least think a bit critically on the subject.I think a firm understanding of statistics is more useful than the entry level calculus and the entry-level science courses like chemistry and biology ( not that those are n't good too , just not as relevant to citizenship ) .Here 's a nice book on statistics called " How To Lie With Statistics " that covers a lot of the ways statistics are misused .
( not a referrer link or anything like that ) http : //www.amazon.com/How-Lie-Statistics-Darrell-Huff/dp/0393310728 [ amazon.com ]</tokentext>
<sentencetext>I've always thought teaching a good understanding of statistics should be a requirement for high schools, since statistics are so often (mis)used to justify public policies and legislation.
We need a citizenry that can see through the bullshit, or at least think a bit critically on the subject.I think a firm understanding of statistics is more useful than the entry level calculus and the entry-level science courses like chemistry and biology(not that those aren't good too, just not as relevant to citizenship).Here's a nice book on statistics called "How To Lie With Statistics" that covers a lot of the ways statistics are misused.
(not a referrer link or anything like that)http://www.amazon.com/How-Lie-Statistics-Darrell-Huff/dp/0393310728 [amazon.com]</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519432</id>
	<title>Re:Long winded troll</title>
	<author>Anonymous</author>
	<datestamp>1268851140000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Have you tried explaining logic to someone (e.g. in the context of tutoring mathematics)?  When you explain something like (p -&gt; q) (not =&gt;) (q -&gt; p) sometimes they will feel insulted...</p></htmltext>
<tokenext>Have you tried explaining logic to someone ( e.g .
in the context of tutoring mathematics ) ?
When you explain something like ( p - &gt; q ) ( not = &gt; ) ( q - &gt; p ) sometimes they will feel insulted.. .</tokentext>
<sentencetext>Have you tried explaining logic to someone (e.g.
in the context of tutoring mathematics)?
When you explain something like (p -&gt; q) (not =&gt;) (q -&gt; p) sometimes they will feel insulted...</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518396</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31522600</id>
	<title>MY common conversation</title>
	<author>Anonymous</author>
	<datestamp>1268926140000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>4</modscore>
	<htmltext><p>The largest demographic in american prisons are black americans. Real statistic but is it true?</p><p>Given a particular sample that indicates blacks are 60\% of the prison population this would appear to be true.</p><p>But what if I said: "The largest demographic in prison is minority, non-whites." Suddenly the \% jumps from 60\% (black) to 80\% (minority). Which is more right? This is the problem with statistics. Context.</p><p>Now I can say readily that the largest demographic in prison is actually right-handed people. The \% now jumps to 90\%.</p><p>But wait! There is more! The largest demographic is prison is actually people who prior to arrest were below the poverty line which jumps to 99\% of the population. Again, all of the above are accurate based on a sample but which is MORE correct? Linear Algebra is coming into play here quickly....</p><p>When that kind of issue comes into play, it is the classic "Correlation != Causation" confusion. The majority of people in prison are in there because of "Being black? Being a minority? being right handed? or being poor?" None of the above. The majority of them are in there because they were convicted of a crime and sentenced. That is the causation of their imprisonment, the rest is correlation which may have a direct causation on the conviction or sentencing, but no direct causation on being in prison. (e.g. You cannot be thrown into prison for being poor, black, minority, right handed)</p><p>Same with medical research, politics, economics, etc. The price of oil rising 10\% and a subsequent 5\% drop in shipping orders. Measuring the significance of regessors is important but oddly never reported most of the time. Many factors get masked or shadowed by higher level regressors (e.g. being a minority masks a variety of other social and economic factors. In addition it can distort statistical work by being too broad. Asians have a variety of different economic and social factors as north american blacks versus even african immigrants.)</p><p>Back to the orignal subject:</p><p>We can take 100 prisoners and 100 non-prisoners and figure out rather quickly if being black is statistically significant in prison population. Non-prison population blacks would account for 25\%-45\% of the population (Depending on location). We can see that 60\% of prisoners are black. There is a 20+\% deviation from the norm. We can test to see the significance of that. Same with minorities. Now we find something quickly that right handed is insignificant because it doesn't deviate from the norm. We can test left-handed and right-handed populations and rule out the handed-ness of a convict being significant.<br>We can find the economic status is considerable MORE significant then minority or black as a status. We can determine that the reason minorities or blacks are disporotinally more prevelant in prison is that blacks and minorities have higher rates of poverty. We can extract and determine the statistical weight of POVERTY in regards to imprisonment (Since we find a high \% of white in prison that are poor compared to the normal population.) Once we figure that out we can remove that and continue an investigation and figure out what weight minority and black has once we have removed POVERTY from the model (Residual analysis).</p><p>The problem in reporting is without providing the whole, comprehensive analysis you can miss important things. For instance to correct the injustice in sentencing, without reporting the weight POVERTY has in contrast to BLACK or MINORITY you may lose sight that you may have better success addressing POVERTY to normalize sentencing rather then MINORITY or BLACK (or not).</p><p>The same happens in medical reasearch. Given a cocktail of drugs wirthout having the whole analysis you may end up providing more of Medicine A versus B but lose sight that A &amp; B are limited by the dosage of Medicine C.</p><p>Satistics are not bullshit, rather mearly observations with no intrinsic agenda or even implication of truth. Purely amoral, like a hand gun.. useful to both the good and evil.</p><p>Statistics don't lie, nor do they tell the truth. They simple show the relationship of the data as it stands. The Truth or Thruthiness of it is subjective and vulnerable to context.</p></htmltext>
<tokenext>The largest demographic in american prisons are black americans .
Real statistic but is it true ? Given a particular sample that indicates blacks are 60 \ % of the prison population this would appear to be true.But what if I said : " The largest demographic in prison is minority , non-whites .
" Suddenly the \ % jumps from 60 \ % ( black ) to 80 \ % ( minority ) .
Which is more right ?
This is the problem with statistics .
Context.Now I can say readily that the largest demographic in prison is actually right-handed people .
The \ % now jumps to 90 \ % .But wait !
There is more !
The largest demographic is prison is actually people who prior to arrest were below the poverty line which jumps to 99 \ % of the population .
Again , all of the above are accurate based on a sample but which is MORE correct ?
Linear Algebra is coming into play here quickly....When that kind of issue comes into play , it is the classic " Correlation ! = Causation " confusion .
The majority of people in prison are in there because of " Being black ?
Being a minority ?
being right handed ?
or being poor ?
" None of the above .
The majority of them are in there because they were convicted of a crime and sentenced .
That is the causation of their imprisonment , the rest is correlation which may have a direct causation on the conviction or sentencing , but no direct causation on being in prison .
( e.g. You can not be thrown into prison for being poor , black , minority , right handed ) Same with medical research , politics , economics , etc .
The price of oil rising 10 \ % and a subsequent 5 \ % drop in shipping orders .
Measuring the significance of regessors is important but oddly never reported most of the time .
Many factors get masked or shadowed by higher level regressors ( e.g .
being a minority masks a variety of other social and economic factors .
In addition it can distort statistical work by being too broad .
Asians have a variety of different economic and social factors as north american blacks versus even african immigrants .
) Back to the orignal subject : We can take 100 prisoners and 100 non-prisoners and figure out rather quickly if being black is statistically significant in prison population .
Non-prison population blacks would account for 25 \ % -45 \ % of the population ( Depending on location ) .
We can see that 60 \ % of prisoners are black .
There is a 20 + \ % deviation from the norm .
We can test to see the significance of that .
Same with minorities .
Now we find something quickly that right handed is insignificant because it does n't deviate from the norm .
We can test left-handed and right-handed populations and rule out the handed-ness of a convict being significant.We can find the economic status is considerable MORE significant then minority or black as a status .
We can determine that the reason minorities or blacks are disporotinally more prevelant in prison is that blacks and minorities have higher rates of poverty .
We can extract and determine the statistical weight of POVERTY in regards to imprisonment ( Since we find a high \ % of white in prison that are poor compared to the normal population .
) Once we figure that out we can remove that and continue an investigation and figure out what weight minority and black has once we have removed POVERTY from the model ( Residual analysis ) .The problem in reporting is without providing the whole , comprehensive analysis you can miss important things .
For instance to correct the injustice in sentencing , without reporting the weight POVERTY has in contrast to BLACK or MINORITY you may lose sight that you may have better success addressing POVERTY to normalize sentencing rather then MINORITY or BLACK ( or not ) .The same happens in medical reasearch .
Given a cocktail of drugs wirthout having the whole analysis you may end up providing more of Medicine A versus B but lose sight that A &amp; B are limited by the dosage of Medicine C.Satistics are not bullshit , rather mearly observations with no intrinsic agenda or even implication of truth .
Purely amoral , like a hand gun.. useful to both the good and evil.Statistics do n't lie , nor do they tell the truth .
They simple show the relationship of the data as it stands .
The Truth or Thruthiness of it is subjective and vulnerable to context .</tokentext>
<sentencetext>The largest demographic in american prisons are black americans.
Real statistic but is it true?Given a particular sample that indicates blacks are 60\% of the prison population this would appear to be true.But what if I said: "The largest demographic in prison is minority, non-whites.
" Suddenly the \% jumps from 60\% (black) to 80\% (minority).
Which is more right?
This is the problem with statistics.
Context.Now I can say readily that the largest demographic in prison is actually right-handed people.
The \% now jumps to 90\%.But wait!
There is more!
The largest demographic is prison is actually people who prior to arrest were below the poverty line which jumps to 99\% of the population.
Again, all of the above are accurate based on a sample but which is MORE correct?
Linear Algebra is coming into play here quickly....When that kind of issue comes into play, it is the classic "Correlation != Causation" confusion.
The majority of people in prison are in there because of "Being black?
Being a minority?
being right handed?
or being poor?
" None of the above.
The majority of them are in there because they were convicted of a crime and sentenced.
That is the causation of their imprisonment, the rest is correlation which may have a direct causation on the conviction or sentencing, but no direct causation on being in prison.
(e.g. You cannot be thrown into prison for being poor, black, minority, right handed)Same with medical research, politics, economics, etc.
The price of oil rising 10\% and a subsequent 5\% drop in shipping orders.
Measuring the significance of regessors is important but oddly never reported most of the time.
Many factors get masked or shadowed by higher level regressors (e.g.
being a minority masks a variety of other social and economic factors.
In addition it can distort statistical work by being too broad.
Asians have a variety of different economic and social factors as north american blacks versus even african immigrants.
)Back to the orignal subject:We can take 100 prisoners and 100 non-prisoners and figure out rather quickly if being black is statistically significant in prison population.
Non-prison population blacks would account for 25\%-45\% of the population (Depending on location).
We can see that 60\% of prisoners are black.
There is a 20+\% deviation from the norm.
We can test to see the significance of that.
Same with minorities.
Now we find something quickly that right handed is insignificant because it doesn't deviate from the norm.
We can test left-handed and right-handed populations and rule out the handed-ness of a convict being significant.We can find the economic status is considerable MORE significant then minority or black as a status.
We can determine that the reason minorities or blacks are disporotinally more prevelant in prison is that blacks and minorities have higher rates of poverty.
We can extract and determine the statistical weight of POVERTY in regards to imprisonment (Since we find a high \% of white in prison that are poor compared to the normal population.
) Once we figure that out we can remove that and continue an investigation and figure out what weight minority and black has once we have removed POVERTY from the model (Residual analysis).The problem in reporting is without providing the whole, comprehensive analysis you can miss important things.
For instance to correct the injustice in sentencing, without reporting the weight POVERTY has in contrast to BLACK or MINORITY you may lose sight that you may have better success addressing POVERTY to normalize sentencing rather then MINORITY or BLACK (or not).The same happens in medical reasearch.
Given a cocktail of drugs wirthout having the whole analysis you may end up providing more of Medicine A versus B but lose sight that A &amp; B are limited by the dosage of Medicine C.Satistics are not bullshit, rather mearly observations with no intrinsic agenda or even implication of truth.
Purely amoral, like a hand gun.. useful to both the good and evil.Statistics don't lie, nor do they tell the truth.
They simple show the relationship of the data as it stands.
The Truth or Thruthiness of it is subjective and vulnerable to context.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31523506</id>
	<title>Re:Looking for a good book on statistics</title>
	<author>Anonymous</author>
	<datestamp>1268930460000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Statistics for scientists and engineers by Myers, Myers and Walpole (Any edition). Long time standard introductory text - lots of examples. If you want to dwelve further into the matter the book by Box and Jenkins called Statistics for experimenters is really good.</p></htmltext>
<tokenext>Statistics for scientists and engineers by Myers , Myers and Walpole ( Any edition ) .
Long time standard introductory text - lots of examples .
If you want to dwelve further into the matter the book by Box and Jenkins called Statistics for experimenters is really good .</tokentext>
<sentencetext>Statistics for scientists and engineers by Myers, Myers and Walpole (Any edition).
Long time standard introductory text - lots of examples.
If you want to dwelve further into the matter the book by Box and Jenkins called Statistics for experimenters is really good.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519618</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31524806</id>
	<title>Re:only in medicine</title>
	<author>Vornzog</author>
	<datestamp>1268936640000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>2</modscore>
	<htmltext><p>I've had my name included on several 'hard science' papers that had horrible statistical assumptions.  I fought, and lost, because my professor had a big grant to maintain, and nobody else understood the underlying assumptions (we used an absolute scaling function, guaranteeing that our distribution was not normal, then tried to assume that it was normal).  The second half of my thesis refutes the math in the last three papers I was on.  Not one single person who read it understood it, which is sad because it wasn't actually all that impressive.</p><p>The only reason I'm not completely ashamed to admit that is that the bad stats don't actually change the conclusions in this case.  They do invalidate the confidence intervals, though...</p><p>The training in stats required for 'hard science' is essentially nil.  Most of the hard science folks I know who are not into high-end mathematical modeling just assume a normal distribution for their data, do a bit of analysis, and publish.  I was in an analytical chemistry lab, where that sort of thing normally works, and to a very high precision.  However, we were working with sloppy biological assays, where being within a factor of two is a miracle.  Under those conditions, you need to know a lot more statistics.</p><p>Basically, the people who know enough math are working on well defined systems and theories, and the medical and biological communities don't know much math at all, but are working on very sloppy systems that need a lot of math to analyze correctly.  It is therefore easier to spot the mistakes in those communities, but don't assume they aren't there in the 'hard science' papers.</p></htmltext>
<tokenext>I 've had my name included on several 'hard science ' papers that had horrible statistical assumptions .
I fought , and lost , because my professor had a big grant to maintain , and nobody else understood the underlying assumptions ( we used an absolute scaling function , guaranteeing that our distribution was not normal , then tried to assume that it was normal ) .
The second half of my thesis refutes the math in the last three papers I was on .
Not one single person who read it understood it , which is sad because it was n't actually all that impressive.The only reason I 'm not completely ashamed to admit that is that the bad stats do n't actually change the conclusions in this case .
They do invalidate the confidence intervals , though...The training in stats required for 'hard science ' is essentially nil .
Most of the hard science folks I know who are not into high-end mathematical modeling just assume a normal distribution for their data , do a bit of analysis , and publish .
I was in an analytical chemistry lab , where that sort of thing normally works , and to a very high precision .
However , we were working with sloppy biological assays , where being within a factor of two is a miracle .
Under those conditions , you need to know a lot more statistics.Basically , the people who know enough math are working on well defined systems and theories , and the medical and biological communities do n't know much math at all , but are working on very sloppy systems that need a lot of math to analyze correctly .
It is therefore easier to spot the mistakes in those communities , but do n't assume they are n't there in the 'hard science ' papers .</tokentext>
<sentencetext>I've had my name included on several 'hard science' papers that had horrible statistical assumptions.
I fought, and lost, because my professor had a big grant to maintain, and nobody else understood the underlying assumptions (we used an absolute scaling function, guaranteeing that our distribution was not normal, then tried to assume that it was normal).
The second half of my thesis refutes the math in the last three papers I was on.
Not one single person who read it understood it, which is sad because it wasn't actually all that impressive.The only reason I'm not completely ashamed to admit that is that the bad stats don't actually change the conclusions in this case.
They do invalidate the confidence intervals, though...The training in stats required for 'hard science' is essentially nil.
Most of the hard science folks I know who are not into high-end mathematical modeling just assume a normal distribution for their data, do a bit of analysis, and publish.
I was in an analytical chemistry lab, where that sort of thing normally works, and to a very high precision.
However, we were working with sloppy biological assays, where being within a factor of two is a miracle.
Under those conditions, you need to know a lot more statistics.Basically, the people who know enough math are working on well defined systems and theories, and the medical and biological communities don't know much math at all, but are working on very sloppy systems that need a lot of math to analyze correctly.
It is therefore easier to spot the mistakes in those communities, but don't assume they aren't there in the 'hard science' papers.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518692</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518608</id>
	<title>bad title</title>
	<author>obliv!on</author>
	<datestamp>1268842200000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>5</modscore>
	<htmltext>It is not a shortcoming of statistics that other people, like various scientists who aren't statisticians, don't know how to use or properly interpret statistics. It is a shortcoming of their knowledge.
<br> <br>
It is not a shortcoming of the Copenhagen interpretation of quantum mechanics or the Chicago school of economics if I don't understand or know how to correctly interpret their results. It is my shortcoming and fault for not knowing enough to connect the dots.
<br> <br>
I do statistical research some of that is through interacting with researchers in the biosciences. Often when I go to talk to a researcher and ask them if they could use some statistical or mathematical or computational assistance with their research it has almost always been a fruitful starting point to long conversations and getting into the research. Now sometimes it was simply a matter of looking at their F-test results or ANOVA scores and telling them what it meant (like with a regression model relating proportions of certain characteristics between taxa), more useful interactions for me often mean working on new algorithms or estimators or working with fitting a model from their empirical data because there isn't a reliable standard model to work off of (like intergenic distance between genes in an operon) that kind of challenge makes less engaging work worth the hassle. Maybe I'm odd because I've worked hard to have a good background in both statistics and biology, but I shouldn't be.
<br> <br>
Although here is an observation that perhaps supports some of the intent of the article from my own experience. I was speaking with a biology graduate student and it came up that they had a biostatistics course in the department. Of course as a statistician my mind goes towards survival function, failure rate, life tables, censored data, bioassy, epidemiology, microarrays, clincal trials, topics along those lines. It turned out their course focused z tests, t tests, f tests, confidence intervals, point predictions, least squares regression, multiple regression, ANOVA, and things along these lines just with simulated problems in a lab setting. That is not necessarily a bad thing, but much of the core math was under played or missing like model assumptions and alternate formulations or things like dummy variables. The worst part was that even though they were doing well with the class they had no confidence in actually using the statistics and didn't understand how to interpret the meaning of something like a confidence interval, they knew how to calculate one, but it wasn't clear what it actually meant to them.
<br> <br>
The corollary to the notion in the summary I'd rant and claim is that scientists overall have less than desirable skills in mathematics, statistics, and computation than those who studied those disciplines principally and that's hurting science. However many in those three disciplines really know little beyond basic results in any of the sciences which hurts the applicability of these mathematical fields to the sciences and likely hurt our ability to develop certain types of discipline specific results that can be generalized from work in application problems.
<br> <br>
In either case whether you're a typical scientist or a typical math/stat/comp person in order to become proficient enough in the other areas it requires going an awfully long out of the way compared to any counterpart who simply does not care and goes straight through as many before have. While in some areas of research on either side it is no problem to do as has been done and not further knowledge into those other areas. Increasingly results that have the highest levels of impact are coming more and more from truly interdisciplinary research. In order to further encourage that for those who are interested in such fields (aside from making more clear what areas in any of the fields fringe to such interdisciplinary work) we need more incentive to study more than one field and/or better ways of enabling fruitful cooperation between the camps.</htmltext>
<tokenext>It is not a shortcoming of statistics that other people , like various scientists who are n't statisticians , do n't know how to use or properly interpret statistics .
It is a shortcoming of their knowledge .
It is not a shortcoming of the Copenhagen interpretation of quantum mechanics or the Chicago school of economics if I do n't understand or know how to correctly interpret their results .
It is my shortcoming and fault for not knowing enough to connect the dots .
I do statistical research some of that is through interacting with researchers in the biosciences .
Often when I go to talk to a researcher and ask them if they could use some statistical or mathematical or computational assistance with their research it has almost always been a fruitful starting point to long conversations and getting into the research .
Now sometimes it was simply a matter of looking at their F-test results or ANOVA scores and telling them what it meant ( like with a regression model relating proportions of certain characteristics between taxa ) , more useful interactions for me often mean working on new algorithms or estimators or working with fitting a model from their empirical data because there is n't a reliable standard model to work off of ( like intergenic distance between genes in an operon ) that kind of challenge makes less engaging work worth the hassle .
Maybe I 'm odd because I 've worked hard to have a good background in both statistics and biology , but I should n't be .
Although here is an observation that perhaps supports some of the intent of the article from my own experience .
I was speaking with a biology graduate student and it came up that they had a biostatistics course in the department .
Of course as a statistician my mind goes towards survival function , failure rate , life tables , censored data , bioassy , epidemiology , microarrays , clincal trials , topics along those lines .
It turned out their course focused z tests , t tests , f tests , confidence intervals , point predictions , least squares regression , multiple regression , ANOVA , and things along these lines just with simulated problems in a lab setting .
That is not necessarily a bad thing , but much of the core math was under played or missing like model assumptions and alternate formulations or things like dummy variables .
The worst part was that even though they were doing well with the class they had no confidence in actually using the statistics and did n't understand how to interpret the meaning of something like a confidence interval , they knew how to calculate one , but it was n't clear what it actually meant to them .
The corollary to the notion in the summary I 'd rant and claim is that scientists overall have less than desirable skills in mathematics , statistics , and computation than those who studied those disciplines principally and that 's hurting science .
However many in those three disciplines really know little beyond basic results in any of the sciences which hurts the applicability of these mathematical fields to the sciences and likely hurt our ability to develop certain types of discipline specific results that can be generalized from work in application problems .
In either case whether you 're a typical scientist or a typical math/stat/comp person in order to become proficient enough in the other areas it requires going an awfully long out of the way compared to any counterpart who simply does not care and goes straight through as many before have .
While in some areas of research on either side it is no problem to do as has been done and not further knowledge into those other areas .
Increasingly results that have the highest levels of impact are coming more and more from truly interdisciplinary research .
In order to further encourage that for those who are interested in such fields ( aside from making more clear what areas in any of the fields fringe to such interdisciplinary work ) we need more incentive to study more than one field and/or better ways of enabling fruitful cooperation between the camps .</tokentext>
<sentencetext>It is not a shortcoming of statistics that other people, like various scientists who aren't statisticians, don't know how to use or properly interpret statistics.
It is a shortcoming of their knowledge.
It is not a shortcoming of the Copenhagen interpretation of quantum mechanics or the Chicago school of economics if I don't understand or know how to correctly interpret their results.
It is my shortcoming and fault for not knowing enough to connect the dots.
I do statistical research some of that is through interacting with researchers in the biosciences.
Often when I go to talk to a researcher and ask them if they could use some statistical or mathematical or computational assistance with their research it has almost always been a fruitful starting point to long conversations and getting into the research.
Now sometimes it was simply a matter of looking at their F-test results or ANOVA scores and telling them what it meant (like with a regression model relating proportions of certain characteristics between taxa), more useful interactions for me often mean working on new algorithms or estimators or working with fitting a model from their empirical data because there isn't a reliable standard model to work off of (like intergenic distance between genes in an operon) that kind of challenge makes less engaging work worth the hassle.
Maybe I'm odd because I've worked hard to have a good background in both statistics and biology, but I shouldn't be.
Although here is an observation that perhaps supports some of the intent of the article from my own experience.
I was speaking with a biology graduate student and it came up that they had a biostatistics course in the department.
Of course as a statistician my mind goes towards survival function, failure rate, life tables, censored data, bioassy, epidemiology, microarrays, clincal trials, topics along those lines.
It turned out their course focused z tests, t tests, f tests, confidence intervals, point predictions, least squares regression, multiple regression, ANOVA, and things along these lines just with simulated problems in a lab setting.
That is not necessarily a bad thing, but much of the core math was under played or missing like model assumptions and alternate formulations or things like dummy variables.
The worst part was that even though they were doing well with the class they had no confidence in actually using the statistics and didn't understand how to interpret the meaning of something like a confidence interval, they knew how to calculate one, but it wasn't clear what it actually meant to them.
The corollary to the notion in the summary I'd rant and claim is that scientists overall have less than desirable skills in mathematics, statistics, and computation than those who studied those disciplines principally and that's hurting science.
However many in those three disciplines really know little beyond basic results in any of the sciences which hurts the applicability of these mathematical fields to the sciences and likely hurt our ability to develop certain types of discipline specific results that can be generalized from work in application problems.
In either case whether you're a typical scientist or a typical math/stat/comp person in order to become proficient enough in the other areas it requires going an awfully long out of the way compared to any counterpart who simply does not care and goes straight through as many before have.
While in some areas of research on either side it is no problem to do as has been done and not further knowledge into those other areas.
Increasingly results that have the highest levels of impact are coming more and more from truly interdisciplinary research.
In order to further encourage that for those who are interested in such fields (aside from making more clear what areas in any of the fields fringe to such interdisciplinary work) we need more incentive to study more than one field and/or better ways of enabling fruitful cooperation between the camps.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519204</id>
	<title>Re:only in medicine</title>
	<author>Idiomatick</author>
	<datestamp>1268848020000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>The softer the science the shittier the stats. Likely for the reasons you listed "the combination of controlling for a lot of variables as well as inadequate mathematics training". Not to offend any social science majors but they often are unable to or unwilling to learn the math required. Not that I think they are stupid, but that the mathematical mind and the social science mind are specced differently. This gap results in terrible stats.</htmltext>
<tokenext>The softer the science the shittier the stats .
Likely for the reasons you listed " the combination of controlling for a lot of variables as well as inadequate mathematics training " .
Not to offend any social science majors but they often are unable to or unwilling to learn the math required .
Not that I think they are stupid , but that the mathematical mind and the social science mind are specced differently .
This gap results in terrible stats .</tokentext>
<sentencetext>The softer the science the shittier the stats.
Likely for the reasons you listed "the combination of controlling for a lot of variables as well as inadequate mathematics training".
Not to offend any social science majors but they often are unable to or unwilling to learn the math required.
Not that I think they are stupid, but that the mathematical mind and the social science mind are specced differently.
This gap results in terrible stats.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518692</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518584</id>
	<title>Countless?</title>
	<author>andr00oo</author>
	<datestamp>1268841960000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>&gt; countless conclusions in the scientific literature are erroneous
<br>
<br>Number of Publications: Finite
<br>Number of Conclusions: Finite
<br>Time taken to count erroneous conclusions: Finite
<br>
<br>Countless Conclusions? I don't think so!
<br>
<br>A large but unspecified number of conclusions in the scientific literature are erroneous: Not so compelling</htmltext>
<tokenext>&gt; countless conclusions in the scientific literature are erroneous Number of Publications : Finite Number of Conclusions : Finite Time taken to count erroneous conclusions : Finite Countless Conclusions ?
I do n't think so !
A large but unspecified number of conclusions in the scientific literature are erroneous : Not so compelling</tokentext>
<sentencetext>&gt; countless conclusions in the scientific literature are erroneous

Number of Publications: Finite
Number of Conclusions: Finite
Time taken to count erroneous conclusions: Finite

Countless Conclusions?
I don't think so!
A large but unspecified number of conclusions in the scientific literature are erroneous: Not so compelling</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521398</id>
	<title>Conclusion...</title>
	<author>ilitirit</author>
	<datestamp>1268919960000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Scientists should start working with statisticians.</htmltext>
<tokenext>Scientists should start working with statisticians .</tokentext>
<sentencetext>Scientists should start working with statisticians.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520142</id>
	<title>Re:Personal experience (central limit theorem)</title>
	<author>whoisisis</author>
	<datestamp>1268906220000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>&gt; And then it struck me - most of the research I had read had applied parametric statistical tests to their data - that it, the researchers made an assumption that the underlying distribution of results would fall on a normal curve.

Which in cases with lots of samples is a perfectly valid assumption. See <a href="http://en.wikipedia.org/wiki/Central\_limit\_theorem" title="wikipedia.org" rel="nofollow">http://en.wikipedia.org/wiki/Central\_limit\_theorem</a> [wikipedia.org]</htmltext>
<tokenext>&gt; And then it struck me - most of the research I had read had applied parametric statistical tests to their data - that it , the researchers made an assumption that the underlying distribution of results would fall on a normal curve .
Which in cases with lots of samples is a perfectly valid assumption .
See http : //en.wikipedia.org/wiki/Central \ _limit \ _theorem [ wikipedia.org ]</tokentext>
<sentencetext>&gt; And then it struck me - most of the research I had read had applied parametric statistical tests to their data - that it, the researchers made an assumption that the underlying distribution of results would fall on a normal curve.
Which in cases with lots of samples is a perfectly valid assumption.
See http://en.wikipedia.org/wiki/Central\_limit\_theorem [wikipedia.org]</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518484</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31533422</id>
	<title>Re:Lies, Damned Lies, and Statistics.</title>
	<author>Anonymous</author>
	<datestamp>1268939820000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>The article never mentioned AGW!</p></htmltext>
<tokenext>The article never mentioned AGW !</tokentext>
<sentencetext>The article never mentioned AGW!</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518344</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518594</id>
	<title>Excellent</title>
	<author>zoso1132</author>
	<datestamp>1268842080000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>2</modscore>
	<htmltext>One of the best articles I've seen on stats (and their misuse).

I'm taking a data analysis course at the moment and I've spent at least a dozen hours simply computing confidence intervals, testing the null hypothesis, and determining significance. It really has changed how I view statistics because it keeps pounding in these very key but oft-ignored principles.</htmltext>
<tokenext>One of the best articles I 've seen on stats ( and their misuse ) .
I 'm taking a data analysis course at the moment and I 've spent at least a dozen hours simply computing confidence intervals , testing the null hypothesis , and determining significance .
It really has changed how I view statistics because it keeps pounding in these very key but oft-ignored principles .</tokentext>
<sentencetext>One of the best articles I've seen on stats (and their misuse).
I'm taking a data analysis course at the moment and I've spent at least a dozen hours simply computing confidence intervals, testing the null hypothesis, and determining significance.
It really has changed how I view statistics because it keeps pounding in these very key but oft-ignored principles.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519914</id>
	<title>Good book about this...</title>
	<author>KingOfSpainIII</author>
	<datestamp>1268945640000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Irrationality by Stuart Sutherland.

Talks about irrationality in general, with a focus on how statistics are generally misunderstood and misused by the public, and particularly health officials.

He also recommends

Innumeracy by John Allen Paulos.

As a good start to learn about statistics and probability theory.</htmltext>
<tokenext>Irrationality by Stuart Sutherland .
Talks about irrationality in general , with a focus on how statistics are generally misunderstood and misused by the public , and particularly health officials .
He also recommends Innumeracy by John Allen Paulos .
As a good start to learn about statistics and probability theory .</tokentext>
<sentencetext>Irrationality by Stuart Sutherland.
Talks about irrationality in general, with a focus on how statistics are generally misunderstood and misused by the public, and particularly health officials.
He also recommends

Innumeracy by John Allen Paulos.
As a good start to learn about statistics and probability theory.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519044</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Jah-Wren Ryel</author>
	<datestamp>1268845980000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>2</modscore>
	<htmltext><p><div class="quote"><p>And then you get studies of the usefulness of psychotropic drugs and wonder whose black hole they pulled that out of...</p></div><p>Indeed.  Normally I would never cite an article in a McNews magazine like Time or Newsweek, but I found this explanation of the state of antidepressant drug efficacy to be one of the best I've run across so far - hundreds of billions of dollars all depending on some really, really bad math.  Its like the collateralized debt securities of the drug &amp; psychiatric industries:</p><p><a href="http://www.newsweek.com/id/232781" title="newsweek.com">http://www.newsweek.com/id/232781</a> [newsweek.com]</p></div>
	</htmltext>
<tokenext>And then you get studies of the usefulness of psychotropic drugs and wonder whose black hole they pulled that out of...Indeed .
Normally I would never cite an article in a McNews magazine like Time or Newsweek , but I found this explanation of the state of antidepressant drug efficacy to be one of the best I 've run across so far - hundreds of billions of dollars all depending on some really , really bad math .
Its like the collateralized debt securities of the drug &amp; psychiatric industries : http : //www.newsweek.com/id/232781 [ newsweek.com ]</tokentext>
<sentencetext>And then you get studies of the usefulness of psychotropic drugs and wonder whose black hole they pulled that out of...Indeed.
Normally I would never cite an article in a McNews magazine like Time or Newsweek, but I found this explanation of the state of antidepressant drug efficacy to be one of the best I've run across so far - hundreds of billions of dollars all depending on some really, really bad math.
Its like the collateralized debt securities of the drug &amp; psychiatric industries:http://www.newsweek.com/id/232781 [newsweek.com]
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518672</id>
	<title>Re:Summery?</title>
	<author>Anonymous</author>
	<datestamp>1268842680000</datestamp>
	<modclass>Funny</modclass>
	<modscore>3</modscore>
	<htmltext>From your sig:<blockquote><div><p>-- Braden's law of data: All data spends some of <b>it's</b> lifetime in an excel spreadsheet.</p></div>
</blockquote><p>What's that law about spelling/grammar corrections inevitably having spelling or grammar mistakes in them?</p></div>
	</htmltext>
<tokenext>From your sig : -- Braden 's law of data : All data spends some of it 's lifetime in an excel spreadsheet .
What 's that law about spelling/grammar corrections inevitably having spelling or grammar mistakes in them ?</tokentext>
<sentencetext>From your sig:-- Braden's law of data: All data spends some of it's lifetime in an excel spreadsheet.
What's that law about spelling/grammar corrections inevitably having spelling or grammar mistakes in them?
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518394</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518718</id>
	<title>Re:Personal experience</title>
	<author>Frequency Domain</author>
	<datestamp>1268843040000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>5</modscore>
	<htmltext><p><div class="quote"><p>...And then it struck me - most of the research I had read had applied parametric statistical tests to their data - that it, the researchers made an assumption that the underlying distribution of results would fall on a normal curve. Yet this simple assumption may be all it takes to skew the data when they should have chosen a non-parametric test instead.</p><p>So yes, stats are vitally important, badly taught, and focus too much on the maths rather than the concepts. Remember that we're doctors, not mathematicians - the last set of sums I did were in high school. If I need to analyse data, I'll probably plug it into SPSS - although now with my eyes open.</p></div><p>That's a good insight.  I'm a statistics professor, and some of the problems I see are a) people generally get exposed to a single course in statistics; b) they're usually mathematically unprepared for it; c) so much gets squeezed into that one opportunity that heads are exploding; d) because of (a) - (c), everybody wants you to "just give 'em the formula"; e) since statistics is so widely used, there's a plethora of courses that are being taught by people who themselves are victims/products of (a) - (d), and are very happy to "just give 'em the formula"; and so e) most people plug and chug data through a stats package with no idea of the applicability, limitations, and interpretation of the results.  The sheer volume of bad analyses is enough to make you weep, and contributes to the widely held perception about "lies, damned lies, and statistics".  And that completely ignores the intentional falsehoods propagated by people who are trying to support various advocacy viewpoints, and will happily mislead the public with biased samples, <a href="http://en.wikipedia.org/wiki/Simpson's\_paradox" title="wikipedia.org">Simpson's paradox</a> [wikipedia.org], invalid assumptions, etc.</p></div>
	</htmltext>
<tokenext>...And then it struck me - most of the research I had read had applied parametric statistical tests to their data - that it , the researchers made an assumption that the underlying distribution of results would fall on a normal curve .
Yet this simple assumption may be all it takes to skew the data when they should have chosen a non-parametric test instead.So yes , stats are vitally important , badly taught , and focus too much on the maths rather than the concepts .
Remember that we 're doctors , not mathematicians - the last set of sums I did were in high school .
If I need to analyse data , I 'll probably plug it into SPSS - although now with my eyes open.That 's a good insight .
I 'm a statistics professor , and some of the problems I see are a ) people generally get exposed to a single course in statistics ; b ) they 're usually mathematically unprepared for it ; c ) so much gets squeezed into that one opportunity that heads are exploding ; d ) because of ( a ) - ( c ) , everybody wants you to " just give 'em the formula " ; e ) since statistics is so widely used , there 's a plethora of courses that are being taught by people who themselves are victims/products of ( a ) - ( d ) , and are very happy to " just give 'em the formula " ; and so e ) most people plug and chug data through a stats package with no idea of the applicability , limitations , and interpretation of the results .
The sheer volume of bad analyses is enough to make you weep , and contributes to the widely held perception about " lies , damned lies , and statistics " .
And that completely ignores the intentional falsehoods propagated by people who are trying to support various advocacy viewpoints , and will happily mislead the public with biased samples , Simpson 's paradox [ wikipedia.org ] , invalid assumptions , etc .</tokentext>
<sentencetext>...And then it struck me - most of the research I had read had applied parametric statistical tests to their data - that it, the researchers made an assumption that the underlying distribution of results would fall on a normal curve.
Yet this simple assumption may be all it takes to skew the data when they should have chosen a non-parametric test instead.So yes, stats are vitally important, badly taught, and focus too much on the maths rather than the concepts.
Remember that we're doctors, not mathematicians - the last set of sums I did were in high school.
If I need to analyse data, I'll probably plug it into SPSS - although now with my eyes open.That's a good insight.
I'm a statistics professor, and some of the problems I see are a) people generally get exposed to a single course in statistics; b) they're usually mathematically unprepared for it; c) so much gets squeezed into that one opportunity that heads are exploding; d) because of (a) - (c), everybody wants you to "just give 'em the formula"; e) since statistics is so widely used, there's a plethora of courses that are being taught by people who themselves are victims/products of (a) - (d), and are very happy to "just give 'em the formula"; and so e) most people plug and chug data through a stats package with no idea of the applicability, limitations, and interpretation of the results.
The sheer volume of bad analyses is enough to make you weep, and contributes to the widely held perception about "lies, damned lies, and statistics".
And that completely ignores the intentional falsehoods propagated by people who are trying to support various advocacy viewpoints, and will happily mislead the public with biased samples, Simpson's paradox [wikipedia.org], invalid assumptions, etc.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518484</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521004</id>
	<title>Re:What it actually said</title>
	<author>thepotoo</author>
	<datestamp>1268917260000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Riiight.  I'll just leave <a href="http://www.uvm.edu/~ngotelli/Bio\%20264/Hurlbert.pdf" title="uvm.edu">this</a> [uvm.edu] here (PDF).</p><p>(P.S. Things haven't gotten better since then.)</p></htmltext>
<tokenext>Riiight .
I 'll just leave this [ uvm.edu ] here ( PDF ) . ( P.S .
Things have n't gotten better since then .
)</tokentext>
<sentencetext>Riiight.
I'll just leave this [uvm.edu] here (PDF).(P.S.
Things haven't gotten better since then.
)</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519396</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518864</id>
	<title>Re:Its common knowledge</title>
	<author>Anonymous</author>
	<datestamp>1268844300000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Luckily, only 34.48\% of the public ever pays attention to statistics. Only 54.13\% of which can properly understand what they mean.</p><p>The world of the average Joe is mean.</p></htmltext>
<tokenext>Luckily , only 34.48 \ % of the public ever pays attention to statistics .
Only 54.13 \ % of which can properly understand what they mean.The world of the average Joe is mean .</tokentext>
<sentencetext>Luckily, only 34.48\% of the public ever pays attention to statistics.
Only 54.13\% of which can properly understand what they mean.The world of the average Joe is mean.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518348</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518958</id>
	<title>Re:Not Scientists</title>
	<author>glwtta</author>
	<datestamp>1268845200000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Wait, I'm sorry, biologists make the lesser contribution to medicine when compared to physicists and engineers? You do realize that <i>all of medicine is biology</i>, right?
<br> <br>
<i>People in the real Sciences would have been forced to take enough Mathematics and/or Statistics to be able to properly interpret Statistics.</i>
<br> <br>
You would think so, if you've never worked with Real Scientists.  Most biologists and chemists (can't speak to the other ones) know just enough statistics to get by, and make exactly the kinds of mistakes TFA is describing - there's only so much you can "force" people to learn.
<br> <br>
<i>Then there's the whole not being able to tell the difference between causation and correlation. I could go on.</i>
<br> <br>
You seriously think this is a common problem in biomedical research?  I mean the actual research, not the media spin on it.</htmltext>
<tokenext>Wait , I 'm sorry , biologists make the lesser contribution to medicine when compared to physicists and engineers ?
You do realize that all of medicine is biology , right ?
People in the real Sciences would have been forced to take enough Mathematics and/or Statistics to be able to properly interpret Statistics .
You would think so , if you 've never worked with Real Scientists .
Most biologists and chemists ( ca n't speak to the other ones ) know just enough statistics to get by , and make exactly the kinds of mistakes TFA is describing - there 's only so much you can " force " people to learn .
Then there 's the whole not being able to tell the difference between causation and correlation .
I could go on .
You seriously think this is a common problem in biomedical research ?
I mean the actual research , not the media spin on it .</tokentext>
<sentencetext>Wait, I'm sorry, biologists make the lesser contribution to medicine when compared to physicists and engineers?
You do realize that all of medicine is biology, right?
People in the real Sciences would have been forced to take enough Mathematics and/or Statistics to be able to properly interpret Statistics.
You would think so, if you've never worked with Real Scientists.
Most biologists and chemists (can't speak to the other ones) know just enough statistics to get by, and make exactly the kinds of mistakes TFA is describing - there's only so much you can "force" people to learn.
Then there's the whole not being able to tell the difference between causation and correlation.
I could go on.
You seriously think this is a common problem in biomedical research?
I mean the actual research, not the media spin on it.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518750</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518818</id>
	<title>What it actually said</title>
	<author>Anonymous</author>
	<datestamp>1268843880000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>5</modscore>
	<htmltext>Contrary to the parent poster's claim, the article does not focus on correlation vs causation.  It focuses on people getting the correlation wrong in the first place.  It lists several common mistakes scientists make when writing up research studies.  (Not all scientists are very good at stats).  These include:
<ul>
<li>If you run enough studies you are almost certain to find a difference that appears statistically significant at the p&lt;0.05 level through chance alone.  (It is incredibly unlikely that you will win the lottery; but across the whole pool of tickets someone wins it most weeks.)  That makes studies that bulk analyze large amounts of data against many different factors, actively hunting for something that is significantly different, erroneous.</li><li>"p &lt; 0.05" does not mean there is a 95\% chance of your result being "true"; it just means that someone else rolling dice has a 5\% chance of achieving the same result through chance alone.</li><li>Tests are often combined in ways that are mathematically inconsistent</li>
<li>Finding a statistical effect does not mean it is a strong effect</li>
<li>You cannot simply compare effect sizes between two studies because the results of their control groups may differ ("effect size analysis" is usually wrong)</li><li>Failing to find a significant effect does not mean there is no effect ("we found there was no significant effect on..." is misleading because "no satistical significance" is "no information" [your study didn't tell anybody anything] not "no effect" -- to prove "no effect" you need a different statistical test)</li></ul><p>And lots of others.  It then suggests Bayesian reasoning as an alternative to traditional statistical tests.</p><p>Most post-PhD scientists are aware of the common mistakes, but being aware that we make mistakes doesn't necessarily stop us from making them.  If you chose a random set of conference proceedings, it is almost certain you will find at least one paper (and I suspect usually a dozen or more) that have statistical mistakes in them.</p></htmltext>
<tokenext>Contrary to the parent poster 's claim , the article does not focus on correlation vs causation .
It focuses on people getting the correlation wrong in the first place .
It lists several common mistakes scientists make when writing up research studies .
( Not all scientists are very good at stats ) .
These include : If you run enough studies you are almost certain to find a difference that appears statistically significant at the p " p Tests are often combined in ways that are mathematically inconsistent Finding a statistical effect does not mean it is a strong effect You can not simply compare effect sizes between two studies because the results of their control groups may differ ( " effect size analysis " is usually wrong ) Failing to find a significant effect does not mean there is no effect ( " we found there was no significant effect on... " is misleading because " no satistical significance " is " no information " [ your study did n't tell anybody anything ] not " no effect " -- to prove " no effect " you need a different statistical test ) And lots of others .
It then suggests Bayesian reasoning as an alternative to traditional statistical tests.Most post-PhD scientists are aware of the common mistakes , but being aware that we make mistakes does n't necessarily stop us from making them .
If you chose a random set of conference proceedings , it is almost certain you will find at least one paper ( and I suspect usually a dozen or more ) that have statistical mistakes in them .</tokentext>
<sentencetext>Contrary to the parent poster's claim, the article does not focus on correlation vs causation.
It focuses on people getting the correlation wrong in the first place.
It lists several common mistakes scientists make when writing up research studies.
(Not all scientists are very good at stats).
These include:

If you run enough studies you are almost certain to find a difference that appears statistically significant at the p"p Tests are often combined in ways that are mathematically inconsistent
Finding a statistical effect does not mean it is a strong effect
You cannot simply compare effect sizes between two studies because the results of their control groups may differ ("effect size analysis" is usually wrong)Failing to find a significant effect does not mean there is no effect ("we found there was no significant effect on..." is misleading because "no satistical significance" is "no information" [your study didn't tell anybody anything] not "no effect" -- to prove "no effect" you need a different statistical test)And lots of others.
It then suggests Bayesian reasoning as an alternative to traditional statistical tests.Most post-PhD scientists are aware of the common mistakes, but being aware that we make mistakes doesn't necessarily stop us from making them.
If you chose a random set of conference proceedings, it is almost certain you will find at least one paper (and I suspect usually a dozen or more) that have statistical mistakes in them.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518396</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518874</id>
	<title>Re:Long winded troll</title>
	<author>TapeCutter</author>
	<datestamp>1268844360000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>It's in the bussiness of providing the best explaination for the available evidence. Proof is confined to axiomatic systems such as maths and generally you can't prove the axioms of axiomatic systems. Science is not an axiomatic system. See <a href="http://en.wikipedia.org/wiki/Epistomology" title="wikipedia.org">epistomology</a> [wikipedia.org] for further details.</htmltext>
<tokenext>It 's in the bussiness of providing the best explaination for the available evidence .
Proof is confined to axiomatic systems such as maths and generally you ca n't prove the axioms of axiomatic systems .
Science is not an axiomatic system .
See epistomology [ wikipedia.org ] for further details .</tokentext>
<sentencetext>It's in the bussiness of providing the best explaination for the available evidence.
Proof is confined to axiomatic systems such as maths and generally you can't prove the axioms of axiomatic systems.
Science is not an axiomatic system.
See epistomology [wikipedia.org] for further details.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518526</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519630</id>
	<title>Re:What it actually said</title>
	<author>TapeCutter</author>
	<datestamp>1268854740000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>2</modscore>
	<htmltext><i>"Contrary to the parent poster's claim, the article does not focus on correlation vs causation. It focuses on people getting the correlation wrong in the first place."</i>
<br> <br>
Fair point, I only skimmed the TFA but I still stand by my assertion that it's a troll of the "scientists don't understand statistics" genre, it even starts by claiming statistics is a "mutant form of math". Had they ommitted that drivel and not refrenced discredited papers then maybe I would have read the whole thing.</htmltext>
<tokenext>" Contrary to the parent poster 's claim , the article does not focus on correlation vs causation .
It focuses on people getting the correlation wrong in the first place .
" Fair point , I only skimmed the TFA but I still stand by my assertion that it 's a troll of the " scientists do n't understand statistics " genre , it even starts by claiming statistics is a " mutant form of math " .
Had they ommitted that drivel and not refrenced discredited papers then maybe I would have read the whole thing .</tokentext>
<sentencetext>"Contrary to the parent poster's claim, the article does not focus on correlation vs causation.
It focuses on people getting the correlation wrong in the first place.
"
 
Fair point, I only skimmed the TFA but I still stand by my assertion that it's a troll of the "scientists don't understand statistics" genre, it even starts by claiming statistics is a "mutant form of math".
Had they ommitted that drivel and not refrenced discredited papers then maybe I would have read the whole thing.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518818</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519562</id>
	<title>Re:Personal experience</title>
	<author>ShakaUVM</author>
	<datestamp>1268853480000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><blockquote><div><p>That's a good insight. I'm a statistics professor, and some of the problems I see are a) people generally get exposed to a single course in statistics; b) they're usually mathematically unprepared for it; c) so much gets squeezed into that one opportunity that heads are exploding; d) because of (a) - (c), everybody wants you to "just give 'em the formula"; e) since statistics is so widely used, there's a plethora of courses that are being taught by people who themselves are victims/products of (a) - (d), and are very happy to "just give 'em the formula"; and so e) most people plug and chug data through a stats package with no idea of the applicability, limitations, and interpretation of the results.</p></div></blockquote><p>I work as an evaluator, doing data analysis of reams of data for school districts, and I won't pretend to be a "stats wizard" - my background is in computer science, though I have a fair bit of stats background - but I get appalled at what I see other evaluators doing.</p><p>On guy, who was presenting at the Federal Department of Education conference had his experimental group of teachers write a test, which they then took. Amazingly enough, they did better than the control group of teachers who took the same test. Other evaluators have teachers listen to a lecture and take a quiz based on the subjects covered on the lecture, and then give the same quiz to a control group. They also do better! Weird, huh? And this is probably the most common methodology used to demonstrate program success in DOE-funded programs today.</p><p>In other words, it is more methodology that appalls me than the stats - in part, because I believe all statistics are somewhat suspect. Do you think that the distribution of people that take tests really fall on a Gaussian? And yet most of the common statistical tests assume Gaussian distributions. Teachers will post the standard deviation of a test result (and use that to "grade on the curve") when the distribution doesn't resemble a bell curve at all.</p><p>Education, in general, doesn't work like administering a drug to the population.</p></div>
	</htmltext>
<tokenext>That 's a good insight .
I 'm a statistics professor , and some of the problems I see are a ) people generally get exposed to a single course in statistics ; b ) they 're usually mathematically unprepared for it ; c ) so much gets squeezed into that one opportunity that heads are exploding ; d ) because of ( a ) - ( c ) , everybody wants you to " just give 'em the formula " ; e ) since statistics is so widely used , there 's a plethora of courses that are being taught by people who themselves are victims/products of ( a ) - ( d ) , and are very happy to " just give 'em the formula " ; and so e ) most people plug and chug data through a stats package with no idea of the applicability , limitations , and interpretation of the results.I work as an evaluator , doing data analysis of reams of data for school districts , and I wo n't pretend to be a " stats wizard " - my background is in computer science , though I have a fair bit of stats background - but I get appalled at what I see other evaluators doing.On guy , who was presenting at the Federal Department of Education conference had his experimental group of teachers write a test , which they then took .
Amazingly enough , they did better than the control group of teachers who took the same test .
Other evaluators have teachers listen to a lecture and take a quiz based on the subjects covered on the lecture , and then give the same quiz to a control group .
They also do better !
Weird , huh ?
And this is probably the most common methodology used to demonstrate program success in DOE-funded programs today.In other words , it is more methodology that appalls me than the stats - in part , because I believe all statistics are somewhat suspect .
Do you think that the distribution of people that take tests really fall on a Gaussian ?
And yet most of the common statistical tests assume Gaussian distributions .
Teachers will post the standard deviation of a test result ( and use that to " grade on the curve " ) when the distribution does n't resemble a bell curve at all.Education , in general , does n't work like administering a drug to the population .</tokentext>
<sentencetext>That's a good insight.
I'm a statistics professor, and some of the problems I see are a) people generally get exposed to a single course in statistics; b) they're usually mathematically unprepared for it; c) so much gets squeezed into that one opportunity that heads are exploding; d) because of (a) - (c), everybody wants you to "just give 'em the formula"; e) since statistics is so widely used, there's a plethora of courses that are being taught by people who themselves are victims/products of (a) - (d), and are very happy to "just give 'em the formula"; and so e) most people plug and chug data through a stats package with no idea of the applicability, limitations, and interpretation of the results.I work as an evaluator, doing data analysis of reams of data for school districts, and I won't pretend to be a "stats wizard" - my background is in computer science, though I have a fair bit of stats background - but I get appalled at what I see other evaluators doing.On guy, who was presenting at the Federal Department of Education conference had his experimental group of teachers write a test, which they then took.
Amazingly enough, they did better than the control group of teachers who took the same test.
Other evaluators have teachers listen to a lecture and take a quiz based on the subjects covered on the lecture, and then give the same quiz to a control group.
They also do better!
Weird, huh?
And this is probably the most common methodology used to demonstrate program success in DOE-funded programs today.In other words, it is more methodology that appalls me than the stats - in part, because I believe all statistics are somewhat suspect.
Do you think that the distribution of people that take tests really fall on a Gaussian?
And yet most of the common statistical tests assume Gaussian distributions.
Teachers will post the standard deviation of a test result (and use that to "grade on the curve") when the distribution doesn't resemble a bell curve at all.Education, in general, doesn't work like administering a drug to the population.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518718</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520556</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268911860000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>As a medical student, we are taught chemistry and biochemistry (not so much physics) including chemical kinetics in both our undergraduate training and in medical school. Your doctor either forgot the basics since he hasn't put them to use or is a dolt.</p><p>As one of the other posters mentioned, we get sporadic lectures on statistics and often it is in 60 minutes or less. Unfortunately, there are more important things that we need to concentrate on and stats just falls on the backburner of "things to do".</p><p>Nonetheless, statistics is useful to glean important information from medical journal articles and justifying whether a particular study is correct or incorrect in its assumptions and conclusions and by association how you can better treat and manage patients.</p></htmltext>
<tokenext>As a medical student , we are taught chemistry and biochemistry ( not so much physics ) including chemical kinetics in both our undergraduate training and in medical school .
Your doctor either forgot the basics since he has n't put them to use or is a dolt.As one of the other posters mentioned , we get sporadic lectures on statistics and often it is in 60 minutes or less .
Unfortunately , there are more important things that we need to concentrate on and stats just falls on the backburner of " things to do " .Nonetheless , statistics is useful to glean important information from medical journal articles and justifying whether a particular study is correct or incorrect in its assumptions and conclusions and by association how you can better treat and manage patients .</tokentext>
<sentencetext>As a medical student, we are taught chemistry and biochemistry (not so much physics) including chemical kinetics in both our undergraduate training and in medical school.
Your doctor either forgot the basics since he hasn't put them to use or is a dolt.As one of the other posters mentioned, we get sporadic lectures on statistics and often it is in 60 minutes or less.
Unfortunately, there are more important things that we need to concentrate on and stats just falls on the backburner of "things to do".Nonetheless, statistics is useful to glean important information from medical journal articles and justifying whether a particular study is correct or incorrect in its assumptions and conclusions and by association how you can better treat and manage patients.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518852</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518802</id>
	<title>Re:Personal experience</title>
	<author>Anonymous</author>
	<datestamp>1268843700000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>I'm more worried by the idea that a doctor is some kind of scientist. They aren't, any more than a car mechanic, plumber or cable guy is.</p></htmltext>
<tokenext>I 'm more worried by the idea that a doctor is some kind of scientist .
They are n't , any more than a car mechanic , plumber or cable guy is .</tokentext>
<sentencetext>I'm more worried by the idea that a doctor is some kind of scientist.
They aren't, any more than a car mechanic, plumber or cable guy is.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518484</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518710</id>
	<title>Re:Its common knowledge</title>
	<author>Anonymous</author>
	<datestamp>1268842920000</datestamp>
	<modclass>Troll</modclass>
	<modscore>-1</modscore>
	<htmltext><p><div class="quote"><p>That 77.28\% of all statistics are made up.</p></div><p>Yes, however in Soviet Russia, 77.28 percent of car analogies are made about YOU!  First post!</p></div>
	</htmltext>
<tokenext>That 77.28 \ % of all statistics are made up.Yes , however in Soviet Russia , 77.28 percent of car analogies are made about YOU !
First post !</tokentext>
<sentencetext>That 77.28\% of all statistics are made up.Yes, however in Soviet Russia, 77.28 percent of car analogies are made about YOU!
First post!
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518348</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518634</id>
	<title>PhD Candidate in Biostatistics Here</title>
	<author>Anonymous</author>
	<datestamp>1268842440000</datestamp>
	<modclass>Troll</modclass>
	<modscore>-1</modscore>
	<htmltext><p>I used to be a devout atheist.  But the more I've learned about science, the more I've learned that it is a giant flimsy pile of assumptions.  In reality, science works just like a religion, with all the same dogma, persecution of questions it doesn't like.  We have a theory of evolution that is being overturned every week, whenever someone finds an old bone in the "wrong" place.  We have a geology built on a faith in isotopes that are supposedly trapped in mud for millions of years, an untestable postulate.  We have a public health system whose job is to tell people what to do, though its recommendations get reversed every few years.  Everybody should use hand sanitizers all the time, oh wait it has no effect.  Table salt should be outlawed, oh wait salt doesn't cause any harm.  Everybody over age 40 should take heart drugs every day, whether they have problems or not -- oh wait, heart pills turn out to have no effect on the risk of heart disease after all.  Everybody should get colonoscopies, oh wait it causes more problems than it solves.  Breast cancer screening, same story.  Everybody should have red ribbons on their car antennas to show support for AIDS victims -- even though diabetes kills far more people and its effects are just as lethal.  So, diabetes victims deserve no sympathy because it is a disease of sloth -- though the same could be said of AIDS.  About 14\% of Americans over age 30 have diabetes, according to this:</p><p><a href="http://www.pophealthmetrics.com/content/7/1/16" title="pophealthmetrics.com" rel="nofollow">http://www.pophealthmetrics.com/content/7/1/16</a> [pophealthmetrics.com]</p><p>That sounds a lot higher than the 0.7\% prevalence of AIDS in America.  Both are incurable but treatable diseases.  Incidentally, there is fairly good evidence that some types of diabetes are caused by viruses such as Coxsackie B4 virus.  But even if it is proven conclusively, I have a feeling it won't produce the same headlines as the discovery of the HIV virus.</p></htmltext>
<tokenext>I used to be a devout atheist .
But the more I 've learned about science , the more I 've learned that it is a giant flimsy pile of assumptions .
In reality , science works just like a religion , with all the same dogma , persecution of questions it does n't like .
We have a theory of evolution that is being overturned every week , whenever someone finds an old bone in the " wrong " place .
We have a geology built on a faith in isotopes that are supposedly trapped in mud for millions of years , an untestable postulate .
We have a public health system whose job is to tell people what to do , though its recommendations get reversed every few years .
Everybody should use hand sanitizers all the time , oh wait it has no effect .
Table salt should be outlawed , oh wait salt does n't cause any harm .
Everybody over age 40 should take heart drugs every day , whether they have problems or not -- oh wait , heart pills turn out to have no effect on the risk of heart disease after all .
Everybody should get colonoscopies , oh wait it causes more problems than it solves .
Breast cancer screening , same story .
Everybody should have red ribbons on their car antennas to show support for AIDS victims -- even though diabetes kills far more people and its effects are just as lethal .
So , diabetes victims deserve no sympathy because it is a disease of sloth -- though the same could be said of AIDS .
About 14 \ % of Americans over age 30 have diabetes , according to this : http : //www.pophealthmetrics.com/content/7/1/16 [ pophealthmetrics.com ] That sounds a lot higher than the 0.7 \ % prevalence of AIDS in America .
Both are incurable but treatable diseases .
Incidentally , there is fairly good evidence that some types of diabetes are caused by viruses such as Coxsackie B4 virus .
But even if it is proven conclusively , I have a feeling it wo n't produce the same headlines as the discovery of the HIV virus .</tokentext>
<sentencetext>I used to be a devout atheist.
But the more I've learned about science, the more I've learned that it is a giant flimsy pile of assumptions.
In reality, science works just like a religion, with all the same dogma, persecution of questions it doesn't like.
We have a theory of evolution that is being overturned every week, whenever someone finds an old bone in the "wrong" place.
We have a geology built on a faith in isotopes that are supposedly trapped in mud for millions of years, an untestable postulate.
We have a public health system whose job is to tell people what to do, though its recommendations get reversed every few years.
Everybody should use hand sanitizers all the time, oh wait it has no effect.
Table salt should be outlawed, oh wait salt doesn't cause any harm.
Everybody over age 40 should take heart drugs every day, whether they have problems or not -- oh wait, heart pills turn out to have no effect on the risk of heart disease after all.
Everybody should get colonoscopies, oh wait it causes more problems than it solves.
Breast cancer screening, same story.
Everybody should have red ribbons on their car antennas to show support for AIDS victims -- even though diabetes kills far more people and its effects are just as lethal.
So, diabetes victims deserve no sympathy because it is a disease of sloth -- though the same could be said of AIDS.
About 14\% of Americans over age 30 have diabetes, according to this:http://www.pophealthmetrics.com/content/7/1/16 [pophealthmetrics.com]That sounds a lot higher than the 0.7\% prevalence of AIDS in America.
Both are incurable but treatable diseases.
Incidentally, there is fairly good evidence that some types of diabetes are caused by viruses such as Coxsackie B4 virus.
But even if it is proven conclusively, I have a feeling it won't produce the same headlines as the discovery of the HIV virus.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520228</id>
	<title>Medicene / Science for money</title>
	<author>SomethingOrOther</author>
	<datestamp>1268907480000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><br> <i>And why would they? They can make more money on Wall Street</i> <br> <br>
Think you are missing the point dude.<br>
We (mostly!) didn't become doctors / scientists to make money. <br> <br>
If people are only motivated by money.... then have you ever wondered why kids climb trees ?</htmltext>
<tokenext>And why would they ?
They can make more money on Wall Street Think you are missing the point dude .
We ( mostly !
) did n't become doctors / scientists to make money .
If people are only motivated by money.... then have you ever wondered why kids climb trees ?</tokentext>
<sentencetext> And why would they?
They can make more money on Wall Street  
Think you are missing the point dude.
We (mostly!
) didn't become doctors / scientists to make money.
If people are only motivated by money.... then have you ever wondered why kids climb trees ?</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518852</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518576</id>
	<title>Study says</title>
	<author>oldhack</author>
	<datestamp>1268841840000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>They're all buncha crap, and I say this with 95\% confidence interval, or sum such stat shit that I wish I can remember.</htmltext>
<tokenext>They 're all buncha crap , and I say this with 95 \ % confidence interval , or sum such stat shit that I wish I can remember .</tokentext>
<sentencetext>They're all buncha crap, and I say this with 95\% confidence interval, or sum such stat shit that I wish I can remember.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520318</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268908620000</datestamp>
	<modclass>Informativ</modclass>
	<modscore>1</modscore>
	<htmltext><p>Are you referring to PSYCHOLOGY or PSYCHIATRY (or both?).</p><p>The former do a lot more stats than the latter during their training. Psychology students in Australia (me) do at least 4 years of statistics (and often 6) before working in the field. Of course we all have a love-hate relationship with stats, but there is no way to get through the course(s) without working hard and actually learning the material.</p><p>Psychiatry students essentially study medicine (and do intermediate stats), and then specialise afterward. They require a completely different skill-set to Psychologists, and have different training.</p></htmltext>
<tokenext>Are you referring to PSYCHOLOGY or PSYCHIATRY ( or both ?
) .The former do a lot more stats than the latter during their training .
Psychology students in Australia ( me ) do at least 4 years of statistics ( and often 6 ) before working in the field .
Of course we all have a love-hate relationship with stats , but there is no way to get through the course ( s ) without working hard and actually learning the material.Psychiatry students essentially study medicine ( and do intermediate stats ) , and then specialise afterward .
They require a completely different skill-set to Psychologists , and have different training .</tokentext>
<sentencetext>Are you referring to PSYCHOLOGY or PSYCHIATRY (or both?
).The former do a lot more stats than the latter during their training.
Psychology students in Australia (me) do at least 4 years of statistics (and often 6) before working in the field.
Of course we all have a love-hate relationship with stats, but there is no way to get through the course(s) without working hard and actually learning the material.Psychiatry students essentially study medicine (and do intermediate stats), and then specialise afterward.
They require a completely different skill-set to Psychologists, and have different training.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519764</id>
	<title>Re:Long winded troll</title>
	<author>Marble1972</author>
	<datestamp>1268943180000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p><div class="quote"><p>science is not in the bussiness of proof</p></div><p>So what is it in the business of?</p></div><p>Excluding mathematics, science is generally in the business of disproof.</p></div>
	</htmltext>
<tokenext>science is not in the bussiness of proofSo what is it in the business of ? Excluding mathematics , science is generally in the business of disproof .</tokentext>
<sentencetext>science is not in the bussiness of proofSo what is it in the business of?Excluding mathematics, science is generally in the business of disproof.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518526</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518640</id>
	<title>Re:Long winded troll</title>
	<author>obliv!on</author>
	<datestamp>1268842440000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Science is in the business of probably knowledge. So they really need to improve their probability and statistics knowledge.</htmltext>
<tokenext>Science is in the business of probably knowledge .
So they really need to improve their probability and statistics knowledge .</tokentext>
<sentencetext>Science is in the business of probably knowledge.
So they really need to improve their probability and statistics knowledge.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518526</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518508</id>
	<title>Rats and Stats</title>
	<author>Anonymous</author>
	<datestamp>1268841360000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>1</modscore>
	<htmltext><p>When I did my BA in psychology Statistics was the core of the degree. It was the one subject that you could not escape and had to take for the full year every year of the degree. I heard later that the Psychology department at that Uni was sometimes disparagingly described as teaching Rats and Stats psychology.</p></htmltext>
<tokenext>When I did my BA in psychology Statistics was the core of the degree .
It was the one subject that you could not escape and had to take for the full year every year of the degree .
I heard later that the Psychology department at that Uni was sometimes disparagingly described as teaching Rats and Stats psychology .</tokentext>
<sentencetext>When I did my BA in psychology Statistics was the core of the degree.
It was the one subject that you could not escape and had to take for the full year every year of the degree.
I heard later that the Psychology department at that Uni was sometimes disparagingly described as teaching Rats and Stats psychology.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519618</id>
	<title>Looking for a good book on statistics</title>
	<author>steveha</author>
	<datestamp>1268854620000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>3</modscore>
	<htmltext><p>I'm interested in learning the essentials of statistics.  What would be a good book to start me out?</p><p>I got <a href="http://nostarch.com/mg\_statistics.htm" title="nostarch.com">The Manga Guide to Statistics</a> [nostarch.com] and it did introduce me to the very basics.  However, there are many places where it just gives you an equation, without deriving it or even explaining it.  After reading this book, I now know how to calculate standard deviation, but I'm still a bit vague on how people actually use it.  I would like to see some examples of how people use statistics in (for example) science experiments.</p><p>My ideal book would explain the basics, with examples, and show how the math works.  Ideally it wouldn't be a thousand pages long, either, but that's a secondary consideration.</p><p>Recommendations, please?</p><p>P.S. Those of you who know about statistics: how good are the Wikipedia pages on statistics?</p><p>steveha</p></htmltext>
<tokenext>I 'm interested in learning the essentials of statistics .
What would be a good book to start me out ? I got The Manga Guide to Statistics [ nostarch.com ] and it did introduce me to the very basics .
However , there are many places where it just gives you an equation , without deriving it or even explaining it .
After reading this book , I now know how to calculate standard deviation , but I 'm still a bit vague on how people actually use it .
I would like to see some examples of how people use statistics in ( for example ) science experiments.My ideal book would explain the basics , with examples , and show how the math works .
Ideally it would n't be a thousand pages long , either , but that 's a secondary consideration.Recommendations , please ? P.S .
Those of you who know about statistics : how good are the Wikipedia pages on statistics ? steveha</tokentext>
<sentencetext>I'm interested in learning the essentials of statistics.
What would be a good book to start me out?I got The Manga Guide to Statistics [nostarch.com] and it did introduce me to the very basics.
However, there are many places where it just gives you an equation, without deriving it or even explaining it.
After reading this book, I now know how to calculate standard deviation, but I'm still a bit vague on how people actually use it.
I would like to see some examples of how people use statistics in (for example) science experiments.My ideal book would explain the basics, with examples, and show how the math works.
Ideally it wouldn't be a thousand pages long, either, but that's a secondary consideration.Recommendations, please?P.S.
Those of you who know about statistics: how good are the Wikipedia pages on statistics?steveha</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521148</id>
	<title>and some psychs "write the book" on statistics</title>
	<author>Anonymous</author>
	<datestamp>1268918340000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p><a href="http://www.amazon.com/How-misuse-statistics-Spectrum-book/dp/0134362047" title="amazon.com" rel="nofollow">http://www.amazon.com/How-misuse-statistics-Spectrum-book/dp/0134362047</a> [amazon.com] was written by an early president of the American Psyhcological Association and, in its day, was often used when teaching lower-level statistics courses.</p></htmltext>
<tokenext>http : //www.amazon.com/How-misuse-statistics-Spectrum-book/dp/0134362047 [ amazon.com ] was written by an early president of the American Psyhcological Association and , in its day , was often used when teaching lower-level statistics courses .</tokentext>
<sentencetext>http://www.amazon.com/How-misuse-statistics-Spectrum-book/dp/0134362047 [amazon.com] was written by an early president of the American Psyhcological Association and, in its day, was often used when teaching lower-level statistics courses.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519492</id>
	<title>Re:Personal experience</title>
	<author>Anonymous</author>
	<datestamp>1268852160000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>"Remember that we're doctors, not mathematicians -"</p><p>Please rephrase this in the form of a Star Trek quote.</p></div>
	</htmltext>
<tokenext>" Remember that we 're doctors , not mathematicians - " Please rephrase this in the form of a Star Trek quote .</tokentext>
<sentencetext>"Remember that we're doctors, not mathematicians -"Please rephrase this in the form of a Star Trek quote.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518484</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520732</id>
	<title>Re:Example: Standard Deviation</title>
	<author>demonlapin</author>
	<datestamp>1268913960000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Not being able to define it with the same precision as someone who works daily in the field is not the same as having no clue what it is, but enjoy your word games, and next time hire a statistician to analyze your blood sugar. He'll be able to tell you all about your statistics, but unless he's diabetic himself, probably won't have a clue what to do about it.<br> <br>I've read your downthread comments, and FWIW biostats in medical school is usually a course involving 36 hours, tops, of instruction.  Most physicians do not need to understand statistics in order to do their jobs, any more than they need to understand how lab equipment generates its results.  I get statistics better than 90\% of physicians, and I'd wilt like a flower in front of a first-year PhD student of the subject.  Why? Because I don't do it all day. You can, and do, forget over time.<br> <br>The ignorance of physicians on statistics is mostly rational ignorance - they've realized that most studies are crap, so they ignore anything that doesn't have lots of patients.  It's only indefensible when they go around trying to spout off like they know what they're talking about - and that is, mercifully, rare.</htmltext>
<tokenext>Not being able to define it with the same precision as someone who works daily in the field is not the same as having no clue what it is , but enjoy your word games , and next time hire a statistician to analyze your blood sugar .
He 'll be able to tell you all about your statistics , but unless he 's diabetic himself , probably wo n't have a clue what to do about it .
I 've read your downthread comments , and FWIW biostats in medical school is usually a course involving 36 hours , tops , of instruction .
Most physicians do not need to understand statistics in order to do their jobs , any more than they need to understand how lab equipment generates its results .
I get statistics better than 90 \ % of physicians , and I 'd wilt like a flower in front of a first-year PhD student of the subject .
Why ? Because I do n't do it all day .
You can , and do , forget over time .
The ignorance of physicians on statistics is mostly rational ignorance - they 've realized that most studies are crap , so they ignore anything that does n't have lots of patients .
It 's only indefensible when they go around trying to spout off like they know what they 're talking about - and that is , mercifully , rare .</tokentext>
<sentencetext>Not being able to define it with the same precision as someone who works daily in the field is not the same as having no clue what it is, but enjoy your word games, and next time hire a statistician to analyze your blood sugar.
He'll be able to tell you all about your statistics, but unless he's diabetic himself, probably won't have a clue what to do about it.
I've read your downthread comments, and FWIW biostats in medical school is usually a course involving 36 hours, tops, of instruction.
Most physicians do not need to understand statistics in order to do their jobs, any more than they need to understand how lab equipment generates its results.
I get statistics better than 90\% of physicians, and I'd wilt like a flower in front of a first-year PhD student of the subject.
Why? Because I don't do it all day.
You can, and do, forget over time.
The ignorance of physicians on statistics is mostly rational ignorance - they've realized that most studies are crap, so they ignore anything that doesn't have lots of patients.
It's only indefensible when they go around trying to spout off like they know what they're talking about - and that is, mercifully, rare.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518402</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518970</id>
	<title>Re:Summery?</title>
	<author>fotoguzzi</author>
	<datestamp>1268845380000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Muphry's Law</htmltext>
<tokenext>Muphry 's Law</tokentext>
<sentencetext>Muphry's Law</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518672</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31523878</id>
	<title>Re:Looking for a good book on statistics</title>
	<author>obliv!on</author>
	<datestamp>1268932320000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Like Daniel Dvorkin has said Devore's book <a href="http://www.google.com/products/catalog?hl=en&amp;source=hp&amp;q=Probability+and+Statistics+for+Engineering+and+the+Sciences&amp;oq=&amp;gs\_rfai=&amp;um=1&amp;ie=UTF-8&amp;cid=10363577541536692493&amp;ei=OUuiS\_vRDYyyNuC3lbkI&amp;sa=X&amp;oi=product\_catalog\_result&amp;ct=result&amp;resnum=3&amp;ved=0CCQQ8wIwAg#ps-sellers" title="google.com" rel="nofollow"> <i>Probability and Statistics for Engineering and the Sciences</i> </a> [google.com] is an excellent starting point.<br> <br>

Definitely learn to use <a href="http://cran.r-project.org/" title="r-project.org" rel="nofollow">R</a> [r-project.org] since its free you don't have to worry about paying licensing fees. It is also widely used (no matter what you here from SAS, Minitab, SPSS, etc).<br> <br>

Books I would recommend that I think fit his other suggestions are Bowerman/O'Connell<a href="http://www.amazon.com/gp/product/0534380182/ref=pd\_lpo\_k2\_dp\_sr\_1?pf\_rd\_p=486539851&amp;pf\_rd\_s=lpo-top-stripe-1&amp;pf\_rd\_t=201&amp;pf\_rd\_i=0534229859&amp;pf\_rd\_m=ATVPDKIKX0DER&amp;pf\_rd\_r=0XRBB6RQ5MV9XSVHPPK1" title="amazon.com" rel="nofollow"> <i>Linear Statistical Models: An Applied Approach</i> </a> [amazon.com] and Wackerly et al <a href="http://www.amazon.com/Mathematical-Statistics-Applications-Dennis-Wackerly/dp/0495110817/ref=sr\_1\_1?ie=UTF8&amp;s=books&amp;qid=1268927612&amp;sr=1-1" title="amazon.com" rel="nofollow"> <i>Mathematical Statistics with Applications</i> </a> [amazon.com] <br> <br>

Devore talks about Bayes Rule as does Wackerly and Wackerly's last chapter talks about some Bayesian techniques, but these are merely primers for what is typical in a Bayesian course. So I recommend these two books as analogous with Devore's: Bolstad <a href="http://www.amazon.com/Introduction-Bayesian-Statistics-William-Bolstad/dp/0470141158/ref=dp\_ob\_title\_bk" title="amazon.com" rel="nofollow"> <i>Introduction to Bayesian Statistics</i> </a> [amazon.com] and to Wackerly's: Hoff <a href="http://www.amazon.com/Bayesian-Statistical-Methods-Springer-Statistics/dp/0387922997/ref=sr\_1\_1?ie=UTF8&amp;s=books&amp;qid=1268927941&amp;sr=1-1" title="amazon.com" rel="nofollow"> <i>A First Course in Bayesian Statistical Methods</i> </a> [amazon.com] <br> <br>

Some things you need from mathematics are the ability to integrate, work with matrices and matrix operations, and algebraic manipulation. Familiarity with transformations and operators especially linear ones is useful since many procedures in statistics are linear operators. The highest levels of statistics will get even more math intense using mathematical results from areas like ODE/PDE, Galios Theory, or general Measure Theory.<br> <br>

The wikipedia's statistics articles are pretty good overall, but as Dvorkin noted some are more technical than what would be friendly to those that are new to statistics. When you feel that's the case try using the sources linked as citations in the article or google confusing parts and it is generally possible to find an explanation for almost any background level.<br> <br>

However if you can get through these texts you're background would be pretty strong.</htmltext>
<tokenext>Like Daniel Dvorkin has said Devore 's book Probability and Statistics for Engineering and the Sciences [ google.com ] is an excellent starting point .
Definitely learn to use R [ r-project.org ] since its free you do n't have to worry about paying licensing fees .
It is also widely used ( no matter what you here from SAS , Minitab , SPSS , etc ) .
Books I would recommend that I think fit his other suggestions are Bowerman/O'Connell Linear Statistical Models : An Applied Approach [ amazon.com ] and Wackerly et al Mathematical Statistics with Applications [ amazon.com ] Devore talks about Bayes Rule as does Wackerly and Wackerly 's last chapter talks about some Bayesian techniques , but these are merely primers for what is typical in a Bayesian course .
So I recommend these two books as analogous with Devore 's : Bolstad Introduction to Bayesian Statistics [ amazon.com ] and to Wackerly 's : Hoff A First Course in Bayesian Statistical Methods [ amazon.com ] Some things you need from mathematics are the ability to integrate , work with matrices and matrix operations , and algebraic manipulation .
Familiarity with transformations and operators especially linear ones is useful since many procedures in statistics are linear operators .
The highest levels of statistics will get even more math intense using mathematical results from areas like ODE/PDE , Galios Theory , or general Measure Theory .
The wikipedia 's statistics articles are pretty good overall , but as Dvorkin noted some are more technical than what would be friendly to those that are new to statistics .
When you feel that 's the case try using the sources linked as citations in the article or google confusing parts and it is generally possible to find an explanation for almost any background level .
However if you can get through these texts you 're background would be pretty strong .</tokentext>
<sentencetext>Like Daniel Dvorkin has said Devore's book  Probability and Statistics for Engineering and the Sciences  [google.com] is an excellent starting point.
Definitely learn to use R [r-project.org] since its free you don't have to worry about paying licensing fees.
It is also widely used (no matter what you here from SAS, Minitab, SPSS, etc).
Books I would recommend that I think fit his other suggestions are Bowerman/O'Connell Linear Statistical Models: An Applied Approach  [amazon.com] and Wackerly et al  Mathematical Statistics with Applications  [amazon.com]  

Devore talks about Bayes Rule as does Wackerly and Wackerly's last chapter talks about some Bayesian techniques, but these are merely primers for what is typical in a Bayesian course.
So I recommend these two books as analogous with Devore's: Bolstad  Introduction to Bayesian Statistics  [amazon.com] and to Wackerly's: Hoff  A First Course in Bayesian Statistical Methods  [amazon.com]  

Some things you need from mathematics are the ability to integrate, work with matrices and matrix operations, and algebraic manipulation.
Familiarity with transformations and operators especially linear ones is useful since many procedures in statistics are linear operators.
The highest levels of statistics will get even more math intense using mathematical results from areas like ODE/PDE, Galios Theory, or general Measure Theory.
The wikipedia's statistics articles are pretty good overall, but as Dvorkin noted some are more technical than what would be friendly to those that are new to statistics.
When you feel that's the case try using the sources linked as citations in the article or google confusing parts and it is generally possible to find an explanation for almost any background level.
However if you can get through these texts you're background would be pretty strong.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519618</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519036</id>
	<title>Re:Example: Standard Deviation</title>
	<author>kevinadi</author>
	<datestamp>1268845920000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Most people I know learn stat by using calculators and computers. Add that to the problem of people's fear toward mathematical formula with big sigma signs and big root squares, and you can be assured that no one would learn stat properly. I seem to get the impression (at least from people I know) that formulas with sigmas are "complex" and they just skip over it. This is a fundamental problem, and I had to explain to some very well educated people to see sigma signs as a "for" loop in computer programs. Then they think it's not so bad after all.</p><p>Stat depends on assumptions, and the assumptions must be stated prior to doing anything, otherwise the analysis in itself is useless. Most stat classes that I took used normal distribution as an assumption, and in many cases it just doesn't apply. The worst that I've seen is someone trying to use the 95\% confidence interval (which is based on normal distribution assumption) on something that I know for certain is Laplacian distributed.</p><p>I think calculators and computers are the worst thing that can happen to statistics learning. It should not be used to learn stat, ever. They encourage people to be hasty, careless, and have the impression that stat is just a collection of magic formulas that gives whatever you want.</p></htmltext>
<tokenext>Most people I know learn stat by using calculators and computers .
Add that to the problem of people 's fear toward mathematical formula with big sigma signs and big root squares , and you can be assured that no one would learn stat properly .
I seem to get the impression ( at least from people I know ) that formulas with sigmas are " complex " and they just skip over it .
This is a fundamental problem , and I had to explain to some very well educated people to see sigma signs as a " for " loop in computer programs .
Then they think it 's not so bad after all.Stat depends on assumptions , and the assumptions must be stated prior to doing anything , otherwise the analysis in itself is useless .
Most stat classes that I took used normal distribution as an assumption , and in many cases it just does n't apply .
The worst that I 've seen is someone trying to use the 95 \ % confidence interval ( which is based on normal distribution assumption ) on something that I know for certain is Laplacian distributed.I think calculators and computers are the worst thing that can happen to statistics learning .
It should not be used to learn stat , ever .
They encourage people to be hasty , careless , and have the impression that stat is just a collection of magic formulas that gives whatever you want .</tokentext>
<sentencetext>Most people I know learn stat by using calculators and computers.
Add that to the problem of people's fear toward mathematical formula with big sigma signs and big root squares, and you can be assured that no one would learn stat properly.
I seem to get the impression (at least from people I know) that formulas with sigmas are "complex" and they just skip over it.
This is a fundamental problem, and I had to explain to some very well educated people to see sigma signs as a "for" loop in computer programs.
Then they think it's not so bad after all.Stat depends on assumptions, and the assumptions must be stated prior to doing anything, otherwise the analysis in itself is useless.
Most stat classes that I took used normal distribution as an assumption, and in many cases it just doesn't apply.
The worst that I've seen is someone trying to use the 95\% confidence interval (which is based on normal distribution assumption) on something that I know for certain is Laplacian distributed.I think calculators and computers are the worst thing that can happen to statistics learning.
It should not be used to learn stat, ever.
They encourage people to be hasty, careless, and have the impression that stat is just a collection of magic formulas that gives whatever you want.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518766</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519856</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268944920000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>2</modscore>
	<htmltext><p>Except, if you had read this story, you would have found that the antidepressant = placebo story to be incorrect due to poor statistical reasoning:<br>"Another concern is the common strategy of combining results from many trials into a single &ldquo;meta-analysis,&rdquo; a study of studies. In a single trial with relatively few participants, statistical tests may not detect small but real and possibly important effects. In principle, combining smaller studies to create a larger sample would allow the tests to detect such small effects. But statistical techniques for doing so are valid only if certain criteria are met. For one thing, all the studies conducted on the drug must be included &mdash; published and unpublished. And all the studies should have been performed in a similar way, using the same protocols, definitions, types of patients and doses. When combining studies with differences, it is necessary first to show that those differences would not affect the analysis, Goodman notes, but that seldom happens. &ldquo;That&rsquo;s not a formal part of most meta-analyses,&rdquo; he says.</p><p>Meta-analyses have produced many controversial conclusions. Common claims that antidepressants work no better than placebos, for example, are based on meta-analyses that do not conform to the criteria that would confer validity. "</p></htmltext>
<tokenext>Except , if you had read this story , you would have found that the antidepressant = placebo story to be incorrect due to poor statistical reasoning : " Another concern is the common strategy of combining results from many trials into a single    meta-analysis ,    a study of studies .
In a single trial with relatively few participants , statistical tests may not detect small but real and possibly important effects .
In principle , combining smaller studies to create a larger sample would allow the tests to detect such small effects .
But statistical techniques for doing so are valid only if certain criteria are met .
For one thing , all the studies conducted on the drug must be included    published and unpublished .
And all the studies should have been performed in a similar way , using the same protocols , definitions , types of patients and doses .
When combining studies with differences , it is necessary first to show that those differences would not affect the analysis , Goodman notes , but that seldom happens .
   That    s not a formal part of most meta-analyses ,    he says.Meta-analyses have produced many controversial conclusions .
Common claims that antidepressants work no better than placebos , for example , are based on meta-analyses that do not conform to the criteria that would confer validity .
"</tokentext>
<sentencetext>Except, if you had read this story, you would have found that the antidepressant = placebo story to be incorrect due to poor statistical reasoning:"Another concern is the common strategy of combining results from many trials into a single “meta-analysis,” a study of studies.
In a single trial with relatively few participants, statistical tests may not detect small but real and possibly important effects.
In principle, combining smaller studies to create a larger sample would allow the tests to detect such small effects.
But statistical techniques for doing so are valid only if certain criteria are met.
For one thing, all the studies conducted on the drug must be included — published and unpublished.
And all the studies should have been performed in a similar way, using the same protocols, definitions, types of patients and doses.
When combining studies with differences, it is necessary first to show that those differences would not affect the analysis, Goodman notes, but that seldom happens.
“That’s not a formal part of most meta-analyses,” he says.Meta-analyses have produced many controversial conclusions.
Common claims that antidepressants work no better than placebos, for example, are based on meta-analyses that do not conform to the criteria that would confer validity.
"</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519044</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519510</id>
	<title>Re:Not Scientists</title>
	<author>NoMaster</author>
	<datestamp>1268852580000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><blockquote><div><blockquote><div><p> <i>People in the real Sciences would have been forced to take enough Mathematics and/or Statistics to be able to properly interpret Statistics.</i></p></div></blockquote><p>You would think so, if you've never worked with Real Scientists. Most biologists and chemists (can't speak to the other ones) know just enough statistics to get by, and make exactly the kinds of mistakes TFA is describing - there's only so much you can "force" people to learn.</p></div></blockquote><p>My favourite example of puncturing the "Real Scientists (tm)" who think they're above making these sorts of mistakes?</p><p><a href="http://www.cscs.umich.edu/~crshalizi/weblog/491.html" title="umich.edu">So You Think You Have a Power Law - Well Isn't That Special?</a> [umich.edu]</p></div>
	</htmltext>
<tokenext>People in the real Sciences would have been forced to take enough Mathematics and/or Statistics to be able to properly interpret Statistics.You would think so , if you 've never worked with Real Scientists .
Most biologists and chemists ( ca n't speak to the other ones ) know just enough statistics to get by , and make exactly the kinds of mistakes TFA is describing - there 's only so much you can " force " people to learn.My favourite example of puncturing the " Real Scientists ( tm ) " who think they 're above making these sorts of mistakes ? So You Think You Have a Power Law - Well Is n't That Special ?
[ umich.edu ]</tokentext>
<sentencetext> People in the real Sciences would have been forced to take enough Mathematics and/or Statistics to be able to properly interpret Statistics.You would think so, if you've never worked with Real Scientists.
Most biologists and chemists (can't speak to the other ones) know just enough statistics to get by, and make exactly the kinds of mistakes TFA is describing - there's only so much you can "force" people to learn.My favourite example of puncturing the "Real Scientists (tm)" who think they're above making these sorts of mistakes?So You Think You Have a Power Law - Well Isn't That Special?
[umich.edu]
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518958</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31522692</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268926500000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>Are you talking about psychiatry or psychology? All psychiatrists are medical doctors (MD or DO). Psychiatry students do not exist...there are however medical doctors completing residency programs in psychiatry. Most psychiatrists are not doing psychotherapy as a primary part of their practice (at least the way you describe it) as they are treating mental illnesses with pharmacologic interventions.</p></htmltext>
<tokenext>Are you talking about psychiatry or psychology ?
All psychiatrists are medical doctors ( MD or DO ) .
Psychiatry students do not exist...there are however medical doctors completing residency programs in psychiatry .
Most psychiatrists are not doing psychotherapy as a primary part of their practice ( at least the way you describe it ) as they are treating mental illnesses with pharmacologic interventions .</tokentext>
<sentencetext>Are you talking about psychiatry or psychology?
All psychiatrists are medical doctors (MD or DO).
Psychiatry students do not exist...there are however medical doctors completing residency programs in psychiatry.
Most psychiatrists are not doing psychotherapy as a primary part of their practice (at least the way you describe it) as they are treating mental illnesses with pharmacologic interventions.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518848</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520220</id>
	<title>Re:Example: Standard Deviation</title>
	<author>ShakaUVM</author>
	<datestamp>1268907360000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><blockquote><div><p>I agree with your concerns. Being a chemical engineer and a physical scientist, I have often found medical doctors understanding of chemistry and other sciences lacking. I once had an argument about chemical kinetics involved in a prescription drug I was taking, he basically told me I didn't know what I was talking about and blew me off. After another run in with him over another issue I fired him. But that's just one of my personal issues with a doctor.</p></div></blockquote><p>That's because you're talking to a doctor when you should be talking to a pharmacist. Preferably a clinical pharmacist, if it's important. The reason hospitals hire clinical pharmacists is because doctors know just enough about drugs to be able to prescribe them correctly, but when they get into trouble (try dosing a renally insufficient diabetic kid some time) they call in the big guns for advice.</p><p>Clinical pharmacists are more common on the west coast than the east coast (having originated in large part at UC San Francisco), but most major hospitals should have them if you have a question.</p><p>You could always pose a question to your community pharmacist or the person shoving drugs at you through the window at the hospital, but depending on how long they've been out of college, they might have forgotten their PK and physical chemistry and such.</p></div>
	</htmltext>
<tokenext>I agree with your concerns .
Being a chemical engineer and a physical scientist , I have often found medical doctors understanding of chemistry and other sciences lacking .
I once had an argument about chemical kinetics involved in a prescription drug I was taking , he basically told me I did n't know what I was talking about and blew me off .
After another run in with him over another issue I fired him .
But that 's just one of my personal issues with a doctor.That 's because you 're talking to a doctor when you should be talking to a pharmacist .
Preferably a clinical pharmacist , if it 's important .
The reason hospitals hire clinical pharmacists is because doctors know just enough about drugs to be able to prescribe them correctly , but when they get into trouble ( try dosing a renally insufficient diabetic kid some time ) they call in the big guns for advice.Clinical pharmacists are more common on the west coast than the east coast ( having originated in large part at UC San Francisco ) , but most major hospitals should have them if you have a question.You could always pose a question to your community pharmacist or the person shoving drugs at you through the window at the hospital , but depending on how long they 've been out of college , they might have forgotten their PK and physical chemistry and such .</tokentext>
<sentencetext>I agree with your concerns.
Being a chemical engineer and a physical scientist, I have often found medical doctors understanding of chemistry and other sciences lacking.
I once had an argument about chemical kinetics involved in a prescription drug I was taking, he basically told me I didn't know what I was talking about and blew me off.
After another run in with him over another issue I fired him.
But that's just one of my personal issues with a doctor.That's because you're talking to a doctor when you should be talking to a pharmacist.
Preferably a clinical pharmacist, if it's important.
The reason hospitals hire clinical pharmacists is because doctors know just enough about drugs to be able to prescribe them correctly, but when they get into trouble (try dosing a renally insufficient diabetic kid some time) they call in the big guns for advice.Clinical pharmacists are more common on the west coast than the east coast (having originated in large part at UC San Francisco), but most major hospitals should have them if you have a question.You could always pose a question to your community pharmacist or the person shoving drugs at you through the window at the hospital, but depending on how long they've been out of college, they might have forgotten their PK and physical chemistry and such.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518852</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518396</id>
	<title>Long winded troll</title>
	<author>Anonymous</author>
	<datestamp>1268840400000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext>The entire article can be summed up by the tiresome cliche "correlation != causation". To make matters worse they quote an economic historian who does not understand that science is not in the bussiness of proof...

<i>"&ldquo;That test itself is neither necessary nor sufficient for proving a scientific result,&rdquo; asserts Stephen Ziliak, an economic historian at Roosevelt University in Chicago."</i></div>
	</htmltext>
<tokenext>The entire article can be summed up by the tiresome cliche " correlation ! = causation " .
To make matters worse they quote an economic historian who does not understand that science is not in the bussiness of proof.. . "    That test itself is neither necessary nor sufficient for proving a scientific result ,    asserts Stephen Ziliak , an economic historian at Roosevelt University in Chicago .
"</tokentext>
<sentencetext>The entire article can be summed up by the tiresome cliche "correlation != causation".
To make matters worse they quote an economic historian who does not understand that science is not in the bussiness of proof...

"“That test itself is neither necessary nor sufficient for proving a scientific result,” asserts Stephen Ziliak, an economic historian at Roosevelt University in Chicago.
"
	</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31532622</id>
	<title>Re:Example: Standard Deviation</title>
	<author>politovski</author>
	<datestamp>1268930520000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>well, not all of us are inept at the physical sciences. and actual m.d.'s are mostly the folks who were always top 5th percentile in everything. but, as for drug kinetics, unfortunately, that rarely if ever predicts how people really respond to a medication. and, most of us really don't understand statistics at all. and as for pharma reps, well, they employ more ex-college cheerleaders than any other industry. and god bless them for that...</p></htmltext>
<tokenext>well , not all of us are inept at the physical sciences .
and actual m.d .
's are mostly the folks who were always top 5th percentile in everything .
but , as for drug kinetics , unfortunately , that rarely if ever predicts how people really respond to a medication .
and , most of us really do n't understand statistics at all .
and as for pharma reps , well , they employ more ex-college cheerleaders than any other industry .
and god bless them for that.. .</tokentext>
<sentencetext>well, not all of us are inept at the physical sciences.
and actual m.d.
's are mostly the folks who were always top 5th percentile in everything.
but, as for drug kinetics, unfortunately, that rarely if ever predicts how people really respond to a medication.
and, most of us really don't understand statistics at all.
and as for pharma reps, well, they employ more ex-college cheerleaders than any other industry.
and god bless them for that...</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518852</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518750</id>
	<title>Not Scientists</title>
	<author>Secret Rabbit</author>
	<datestamp>1268843280000</datestamp>
	<modclass>Flamebait</modclass>
	<modscore>0</modscore>
	<htmltext><p>Ok, so the referenced fields that have problem with stats are both not Sciences.  Medicine has no theories that govern the human body.  All they do is memorize a bunch of crap and then poke some squishy bits and memorize how it looks and feels when healthy/normal v.s. unhealthy/abnormal.  It's really the Engineers, Physicists, Chemists and to a lesser extent (though they are gaining market-share) Biologists, that make the true breakthroughs in Medicine.</p><p>And the social "sciences" are just plain an embarrassment when it compares to real Science.  Seriously...</p><p>People in the real Sciences would have been forced to take enough Mathematics and/or Statistics to be able to properly interpret Statistics.  And just as importantly, be able to do proper experiment design (Medicine, I'm looking at you).  Then there's the whole not being able to tell the difference between causation and correlation.  I could go on.</p></htmltext>
<tokenext>Ok , so the referenced fields that have problem with stats are both not Sciences .
Medicine has no theories that govern the human body .
All they do is memorize a bunch of crap and then poke some squishy bits and memorize how it looks and feels when healthy/normal v.s .
unhealthy/abnormal. It 's really the Engineers , Physicists , Chemists and to a lesser extent ( though they are gaining market-share ) Biologists , that make the true breakthroughs in Medicine.And the social " sciences " are just plain an embarrassment when it compares to real Science .
Seriously...People in the real Sciences would have been forced to take enough Mathematics and/or Statistics to be able to properly interpret Statistics .
And just as importantly , be able to do proper experiment design ( Medicine , I 'm looking at you ) .
Then there 's the whole not being able to tell the difference between causation and correlation .
I could go on .</tokentext>
<sentencetext>Ok, so the referenced fields that have problem with stats are both not Sciences.
Medicine has no theories that govern the human body.
All they do is memorize a bunch of crap and then poke some squishy bits and memorize how it looks and feels when healthy/normal v.s.
unhealthy/abnormal.  It's really the Engineers, Physicists, Chemists and to a lesser extent (though they are gaining market-share) Biologists, that make the true breakthroughs in Medicine.And the social "sciences" are just plain an embarrassment when it compares to real Science.
Seriously...People in the real Sciences would have been forced to take enough Mathematics and/or Statistics to be able to properly interpret Statistics.
And just as importantly, be able to do proper experiment design (Medicine, I'm looking at you).
Then there's the whole not being able to tell the difference between causation and correlation.
I could go on.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519682</id>
	<title>Re:only in medicine</title>
	<author>Anonymous</author>
	<datestamp>1268855820000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>No, medicine is not an isolated case here.</p><p>I have friends working for institution where chemistry and physics get involved and they tell me that it is just as often in their papers too. The thing is, there is lot more involved/at stake in magnetic resonance imaging of the humans than in MRI of one molecule. Just ask lawyers.</p></htmltext>
<tokenext>No , medicine is not an isolated case here.I have friends working for institution where chemistry and physics get involved and they tell me that it is just as often in their papers too .
The thing is , there is lot more involved/at stake in magnetic resonance imaging of the humans than in MRI of one molecule .
Just ask lawyers .</tokentext>
<sentencetext>No, medicine is not an isolated case here.I have friends working for institution where chemistry and physics get involved and they tell me that it is just as often in their papers too.
The thing is, there is lot more involved/at stake in magnetic resonance imaging of the humans than in MRI of one molecule.
Just ask lawyers.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518692</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518486</id>
	<title>Pirates cause cool weather</title>
	<author>wisnoskij</author>
	<datestamp>1268841180000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Funny Stat correlation:
<a href="http://www.seanbonner.com/blog/archives/piratesarecool.jpg" title="seanbonner.com">http://www.seanbonner.com/blog/archives/piratesarecool.jpg</a> [seanbonner.com]</htmltext>
<tokenext>Funny Stat correlation : http : //www.seanbonner.com/blog/archives/piratesarecool.jpg [ seanbonner.com ]</tokentext>
<sentencetext>Funny Stat correlation:
http://www.seanbonner.com/blog/archives/piratesarecool.jpg [seanbonner.com]</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31520434</id>
	<title>Re:Example: Standard Deviation</title>
	<author>u38cg</author>
	<datestamp>1268910480000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext>Indeed.  A particular irk of mine is that people often quote a standard deviation when talking about some variable that is not normally distributed, and then make probabilistic claims based on the cumulative distribution of the normal!</div>
	</htmltext>
<tokenext>Indeed .
A particular irk of mine is that people often quote a standard deviation when talking about some variable that is not normally distributed , and then make probabilistic claims based on the cumulative distribution of the normal !</tokentext>
<sentencetext>Indeed.
A particular irk of mine is that people often quote a standard deviation when talking about some variable that is not normally distributed, and then make probabilistic claims based on the cumulative distribution of the normal!
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518402</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518402</id>
	<title>Example: Standard Deviation</title>
	<author>cytoman</author>
	<datestamp>1268840520000</datestamp>
	<modclass>Interestin</modclass>
	<modscore>4</modscore>
	<htmltext>My doctor was explaining to me that my blood sugar readings should not have a standard deviation of more than 1/3rd of the average blood sugar reading. Just to test if he knew what it meant, I asked him what a standard deviation was. Oh the fun when he tried to bullshit his way out of that one! He eventually told me that when I plot my data in Excel I can ask it to give me statistics on the column and it would mention what the standard deviation value was. But when I pressed on and asked him what a standard deviation is, he shooed me off and told me to go look it up. Never did he confess that he had no clue.</htmltext>
<tokenext>My doctor was explaining to me that my blood sugar readings should not have a standard deviation of more than 1/3rd of the average blood sugar reading .
Just to test if he knew what it meant , I asked him what a standard deviation was .
Oh the fun when he tried to bullshit his way out of that one !
He eventually told me that when I plot my data in Excel I can ask it to give me statistics on the column and it would mention what the standard deviation value was .
But when I pressed on and asked him what a standard deviation is , he shooed me off and told me to go look it up .
Never did he confess that he had no clue .</tokentext>
<sentencetext>My doctor was explaining to me that my blood sugar readings should not have a standard deviation of more than 1/3rd of the average blood sugar reading.
Just to test if he knew what it meant, I asked him what a standard deviation was.
Oh the fun when he tried to bullshit his way out of that one!
He eventually told me that when I plot my data in Excel I can ask it to give me statistics on the column and it would mention what the standard deviation value was.
But when I pressed on and asked him what a standard deviation is, he shooed me off and told me to go look it up.
Never did he confess that he had no clue.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518580</id>
	<title>Re:Long winded troll</title>
	<author>Anonymous</author>
	<datestamp>1268841900000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>0</modscore>
	<htmltext><p>No it can't. The article does a fairly good job at summarizing the systematic conceptual mistake of misinterpreting a p-value as representing a probability that the hypothesis is not true, among other things, and a somewhat less good job at introducing Bayesian statistics. These are subtler issues than the true-but-trivial&mdash;and tiresome&mdash;clich&#233; you refer to.</p></htmltext>
<tokenext>No it ca n't .
The article does a fairly good job at summarizing the systematic conceptual mistake of misinterpreting a p-value as representing a probability that the hypothesis is not true , among other things , and a somewhat less good job at introducing Bayesian statistics .
These are subtler issues than the true-but-trivial    and tiresome    clich   you refer to .</tokentext>
<sentencetext>No it can't.
The article does a fairly good job at summarizing the systematic conceptual mistake of misinterpreting a p-value as representing a probability that the hypothesis is not true, among other things, and a somewhat less good job at introducing Bayesian statistics.
These are subtler issues than the true-but-trivial—and tiresome—cliché you refer to.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518396</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31519224</id>
	<title>Re:The problem is statisticians</title>
	<author>hoytak</author>
	<datestamp>1268848320000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p>Maybe, but often common sense will lead even the most diligent scientist astray.  Many times the answer that "just can't be right" is; the problem comes when we "throw away the statistics" instead of figuring out why and how it gave the answer it did.</p><p>Furthermore, variances and probabilities and confidence intervals are often discarded in favor of a point answer.  It's an unfortunate reality; properly done statistics very nicely captures real life uncertainties, but the untrained eye or the popular media doesn't work that way.</p><p>I think the ideal solution is for the general technical culture to become both more knowledgeable about basic probability and less accepting of bad statistics (e.g. the world WILL be 5 degrees warmer in 2050).  This will encourage people to really understand the procedures they are using.  And yes, there are many fronts to this problem -- as a phd student in statistics I'm well aware of the complexities involved -- but I do trust statistics more than my common sense.</p></htmltext>
<tokenext>Maybe , but often common sense will lead even the most diligent scientist astray .
Many times the answer that " just ca n't be right " is ; the problem comes when we " throw away the statistics " instead of figuring out why and how it gave the answer it did.Furthermore , variances and probabilities and confidence intervals are often discarded in favor of a point answer .
It 's an unfortunate reality ; properly done statistics very nicely captures real life uncertainties , but the untrained eye or the popular media does n't work that way.I think the ideal solution is for the general technical culture to become both more knowledgeable about basic probability and less accepting of bad statistics ( e.g .
the world WILL be 5 degrees warmer in 2050 ) .
This will encourage people to really understand the procedures they are using .
And yes , there are many fronts to this problem -- as a phd student in statistics I 'm well aware of the complexities involved -- but I do trust statistics more than my common sense .</tokentext>
<sentencetext>Maybe, but often common sense will lead even the most diligent scientist astray.
Many times the answer that "just can't be right" is; the problem comes when we "throw away the statistics" instead of figuring out why and how it gave the answer it did.Furthermore, variances and probabilities and confidence intervals are often discarded in favor of a point answer.
It's an unfortunate reality; properly done statistics very nicely captures real life uncertainties, but the untrained eye or the popular media doesn't work that way.I think the ideal solution is for the general technical culture to become both more knowledgeable about basic probability and less accepting of bad statistics (e.g.
the world WILL be 5 degrees warmer in 2050).
This will encourage people to really understand the procedures they are using.
And yes, there are many fronts to this problem -- as a phd student in statistics I'm well aware of the complexities involved -- but I do trust statistics more than my common sense.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518544</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518524</id>
	<title>Fair and Balanced: Fox quotes the Bible as saying</title>
	<author>vandelais</author>
	<datestamp>1268841420000</datestamp>
	<modclass>Funny</modclass>
	<modscore>2</modscore>
	<htmltext><p>that there are only 3 kinds of scientists:  those that are good at math and those that aren't.</p></htmltext>
<tokenext>that there are only 3 kinds of scientists : those that are good at math and those that are n't .</tokentext>
<sentencetext>that there are only 3 kinds of scientists:  those that are good at math and those that aren't.</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521668</id>
	<title>Oh, yes!</title>
	<author>Anonymous</author>
	<datestamp>1268921580000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>When I studied medicine a few years ago I was surprised to see my fellow students not understanding the easiest mathematical tests and their implications.  But the university wasn't of any help.  Instead of telling the students about the importance of these tests and showing them how and where to get help to get correct tests, they declared this knowledge as not important for becoming a physician.  So nobody even tried to understand these tests, the courses were just lost time.  Later these students used software to create charts which looked great.  The fact that they were wrong was of minor importance, as nobody understood or checked them.</p><p>cb</p></htmltext>
<tokenext>When I studied medicine a few years ago I was surprised to see my fellow students not understanding the easiest mathematical tests and their implications .
But the university was n't of any help .
Instead of telling the students about the importance of these tests and showing them how and where to get help to get correct tests , they declared this knowledge as not important for becoming a physician .
So nobody even tried to understand these tests , the courses were just lost time .
Later these students used software to create charts which looked great .
The fact that they were wrong was of minor importance , as nobody understood or checked them.cb</tokentext>
<sentencetext>When I studied medicine a few years ago I was surprised to see my fellow students not understanding the easiest mathematical tests and their implications.
But the university wasn't of any help.
Instead of telling the students about the importance of these tests and showing them how and where to get help to get correct tests, they declared this knowledge as not important for becoming a physician.
So nobody even tried to understand these tests, the courses were just lost time.
Later these students used software to create charts which looked great.
The fact that they were wrong was of minor importance, as nobody understood or checked them.cb</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518850</id>
	<title>Re:Lies, Damned Lies, and Statistics.</title>
	<author>Lars T.</author>
	<datestamp>1268844180000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>and there are statistically two popes per square kilometer in the vatican.</p></div><p>But the expected value of popes per Vatican City is still one.</p></div>
	</htmltext>
<tokenext>and there are statistically two popes per square kilometer in the vatican.But the expected value of popes per Vatican City is still one .</tokentext>
<sentencetext>and there are statistically two popes per square kilometer in the vatican.But the expected value of popes per Vatican City is still one.
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518344</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518544</id>
	<title>The problem is statisticians</title>
	<author>Anonymous</author>
	<datestamp>1268841660000</datestamp>
	<modclass>Insightful</modclass>
	<modscore>5</modscore>
	<htmltext><i>In other news math may not lie but people still can...</i> <br> <br>
Usually (in science at least) it's not even a matter of lying. Part of the problem is that the multi-headed monster that statistics has become has a tendency to lead people to over-use numerical "answers" vomited up by stats packages, without really understanding what they are for, or how to interpret them.<br> <br>
Statistics are very useful for <i>predicting</i> certain things, but all too often they are submitted as "proof" of a given condition, which is dangerous. Sometimes we need to throw away statistics and start applying common sense.</htmltext>
<tokenext>In other news math may not lie but people still can.. . Usually ( in science at least ) it 's not even a matter of lying .
Part of the problem is that the multi-headed monster that statistics has become has a tendency to lead people to over-use numerical " answers " vomited up by stats packages , without really understanding what they are for , or how to interpret them .
Statistics are very useful for predicting certain things , but all too often they are submitted as " proof " of a given condition , which is dangerous .
Sometimes we need to throw away statistics and start applying common sense .</tokentext>
<sentencetext>In other news math may not lie but people still can...  
Usually (in science at least) it's not even a matter of lying.
Part of the problem is that the multi-headed monster that statistics has become has a tendency to lead people to over-use numerical "answers" vomited up by stats packages, without really understanding what they are for, or how to interpret them.
Statistics are very useful for predicting certain things, but all too often they are submitted as "proof" of a given condition, which is dangerous.
Sometimes we need to throw away statistics and start applying common sense.</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518344</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31521650</id>
	<title>Its not the scientists, its the statisticians</title>
	<author>Anonymous</author>
	<datestamp>1268921460000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p>when people can't use a basic tool, its the fault of the tool, not the people<br>As wolfgang pauli remarked, its not that new ideas triumph because people discard old ideas; new ideas triumph because old people die and the students learn the correct idea in the 1st place</p></htmltext>
<tokenext>when people ca n't use a basic tool , its the fault of the tool , not the peopleAs wolfgang pauli remarked , its not that new ideas triumph because people discard old ideas ; new ideas triumph because old people die and the students learn the correct idea in the 1st place</tokentext>
<sentencetext>when people can't use a basic tool, its the fault of the tool, not the peopleAs wolfgang pauli remarked, its not that new ideas triumph because people discard old ideas; new ideas triumph because old people die and the students learn the correct idea in the 1st place</sentencetext>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31525884</id>
	<title>Re:Example: Standard Deviation</title>
	<author>Anonymous</author>
	<datestamp>1268941020000</datestamp>
	<modclass>None</modclass>
	<modscore>0</modscore>
	<htmltext><p><div class="quote"><p>My doctor was explaining to me that my blood sugar readings should not have a standard deviation of more than 1/3rd of the average blood sugar reading.<nobr> <wbr></nobr>...</p></div><p>Hmm.  It seems to me that he was focused on getting your blood sugar where it needed to be, but your concern was how well he remembered his biostat classes.  He was telling you what your blood sugar numbers should be (and yes, he did know what the right numbers were), and you hijacked the conversation to talk about math.  Could it be that you didn't want to talk about boring stuff like--- changing your diet, keeping your weight down, taking your medication<nobr> <wbr></nobr>...</p></div>
	</htmltext>
<tokenext>My doctor was explaining to me that my blood sugar readings should not have a standard deviation of more than 1/3rd of the average blood sugar reading .
...Hmm. It seems to me that he was focused on getting your blood sugar where it needed to be , but your concern was how well he remembered his biostat classes .
He was telling you what your blood sugar numbers should be ( and yes , he did know what the right numbers were ) , and you hijacked the conversation to talk about math .
Could it be that you did n't want to talk about boring stuff like--- changing your diet , keeping your weight down , taking your medication .. .</tokentext>
<sentencetext>My doctor was explaining to me that my blood sugar readings should not have a standard deviation of more than 1/3rd of the average blood sugar reading.
...Hmm.  It seems to me that he was focused on getting your blood sugar where it needed to be, but your concern was how well he remembered his biostat classes.
He was telling you what your blood sugar numbers should be (and yes, he did know what the right numbers were), and you hijacked the conversation to talk about math.
Could it be that you didn't want to talk about boring stuff like--- changing your diet, keeping your weight down, taking your medication ...
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518402</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518490</id>
	<title>Re:Long winded troll</title>
	<author>Nefarious Wheel</author>
	<datestamp>1268841180000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>The entire article can be summed up by the tiresome cliche "correlation != causation"...</p></div><p>The logical fallacy is called "post hoc, ergo propter hoc" - "after this, therefore because of this".  </p><p>Sort of like - I get a headache every time someone turns on the television, therefore headaches are caused by the television.</p><p>Oh, hang on...</p></div>
	</htmltext>
<tokenext>The entire article can be summed up by the tiresome cliche " correlation ! = causation " ...The logical fallacy is called " post hoc , ergo propter hoc " - " after this , therefore because of this " .
Sort of like - I get a headache every time someone turns on the television , therefore headaches are caused by the television.Oh , hang on.. .</tokentext>
<sentencetext>The entire article can be summed up by the tiresome cliche "correlation != causation"...The logical fallacy is called "post hoc, ergo propter hoc" - "after this, therefore because of this".
Sort of like - I get a headache every time someone turns on the television, therefore headaches are caused by the television.Oh, hang on...
	</sentencetext>
	<parent>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31518396</parent>
</comment>
<comment>
	<id>http://www.semanticweb.org/ontologies/ConversationInstances.owl#comment10_03_17_2239252.31573474</id>
	<title>Re:Personal experience</title>
	<author>wfolta</author>
	<datestamp>1269288360000</datestamp>
	<modclass>None</modclass>
	<modscore>1</modscore>
	<htmltext><p><div class="quote"><p>That's a good insight.  I'm a statistics professor, and some of the problems I see are a) people generally get exposed to a single course in statistics; b) they're usually mathematically unprepared for it; c) so much gets squeezed into that one opportunity that heads are exploding;...</p></div><p>I'd add that the head 'sploding part, for me, was probability. I wasn't really able to overcome probability's counter-intuitive nature in the first half of Probability &amp; Statistics, so I managed to create a really-sketchy-but-pulled-my-class-grade-average-up-from-dismal picture of statistics that turned burned me on my next two or three exposures to stats in other areas... I was well into graduate school before I finally started getting it.</p></div>
	</htmltext>
<tokenext>That 's a good insight .
I 'm a statistics professor , and some of the problems I see are a ) people generally get exposed to a single course in statistics ; b ) they 're usually mathematically unprepared for it ; c ) so much gets squeezed into that one opportunity that heads are exploding ; ...I 'd add that the head 'sploding part , for me , was probability .
I was n't really able to overcome probability 's counter-intuitive nature in the first half of Probability &amp; Statistics , so I managed to create a really-sketchy-but-pulled-my-class-grade-average-up-from-dismal picture of statistics that turned burned me on my next two or three exposures to stats in other areas... I was well into graduate school before I finally started getting it .</tokentext>
<sentencetext>That's a good insight.
I'm a statistics professor, and some of the problems I see are a) people generally get exposed to a single course in statistics; b) they're usually mathematically unprepared for it; c) so much gets squeezed into that one opportunity that heads are exploding;...I'd add that the head 'sploding part, for me, was probability.
I wasn't really able to overcome probability's counter-intuitive nature in the first half of Probability &amp; Statistics, so I managed to create a really-sketchy-but-pulled-my-class-grade-average-up-from-dismal picture of statistics that turned burned me on my next two or three exposures to stats in other areas... I was well into graduate school before I finally started getting it.
	</sentencetext>
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