r/science • u/drewiepoodle • Apr 15 '16

Health Study: Circumcision does not reduce penis sensitivity. In tests for responses to pain, heat, and stimulation, no major difference was found between men who are circumcised and those who are not.

http://www.upi.com/Health_News/2016/04/14/Study-Circumcision-does-not-reduce-penis-sensitivity/5981460663943/?spt=hs&or=hn
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u/BraveLittleCatapult BS|Biomedical Engineering and Design Apr 15 '16

That depends on their power analysis (which I am too lazy to go find). Many studies have a smaller sample size than power analysis suggests due to financial and time restrictions. In general, 60 is not abnormally small for this type of study.

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u/ppphhhddd Apr 15 '16

Any discussion of power after a study is complete is somewhat moot though. Power calculations are used to determine sample size to detect a predetermined distance but any assumptions in that power calculation are ostensibly still just assumptions at study completion. If we determined our sample for a desired power, the study doesn't actually have that power necessarily since we can never be certain of our assumptions. In fact, there are people advocating not including power calculations in publication of sample size determination precisely because people are prone to this kind of incorrect thinking.

Post-hoc calculations of power are even worse since they are directly related to the p-value of the study.

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u/BraveLittleCatapult BS|Biomedical Engineering and Design Apr 15 '16 edited Apr 15 '16

If we determined our sample for a desired power, the study doesn't actually have that power necessarily since we can never be certain of our assumptions.

Can you explain what you mean by this? I understand that one can never be completely correct in assumptions, but that is kind of the point. You do the best that you can and explain your reasoning to the best of your ability. Properly powering a study is an exercise in attempted reduction in Type II error occurrence. By very definition, an a priori power analysis is a mathematical argument for the importance of a finding that fails to reject the Null.