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In layman's terms, the comic appears to misrepresent what "95% confidence" (p <0.05) means.  The statistic "p < 0.05" means that when we find a correlation based on data, that correlation will be a false positive fewer than 5 percent of the time.  In other words, when we observe the correlation in the data, that correlation actually exists in the real world at least 19 out of 20 times.  It '''does not''' mean that 1 out of every 20 tests will produce a false positive.  This comic displays a pretty significant failure in understanding of Bayesian mathematics.  The 5% chance isn't a 5% chance that any test will produce a (false) positive; it's a 5% chance that a statistical positive is a false positive. {{unsigned ip|108.162.219.196}}
 
In layman's terms, the comic appears to misrepresent what "95% confidence" (p <0.05) means.  The statistic "p < 0.05" means that when we find a correlation based on data, that correlation will be a false positive fewer than 5 percent of the time.  In other words, when we observe the correlation in the data, that correlation actually exists in the real world at least 19 out of 20 times.  It '''does not''' mean that 1 out of every 20 tests will produce a false positive.  This comic displays a pretty significant failure in understanding of Bayesian mathematics.  The 5% chance isn't a 5% chance that any test will produce a (false) positive; it's a 5% chance that a statistical positive is a false positive. {{unsigned ip|108.162.219.196}}
  
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:No, you are deeply mistaken. The comic and the comment above you are correct in saying that if the null hypothesis holds, 1 out of every 20 tests will produce a false positive: this is by definition of the p-value. The ratio of true positives to false positives can range anywhere from 0 to infinity, and there is unfortunately no way to predict it. [[Special:Contributions/108.162.229.121|108.162.229.121]] 09:46, 27 January 2015 (UTC)
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:No, you are deeply mistaken. The comic and the comment above you are correct in saying that 1 out of every 20 tests will produce a false positive: this is by definition of the p-value. The ratio of true positives to false positives can range anywhere from 0 to infinity, and there is unfortunately no way to predict it. [[Special:Contributions/108.162.229.121|108.162.229.121]] 09:46, 27 January 2015 (UTC)
  
  

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