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You had to introduce a lot of assumptions to invalidate the n=31. So let me lay it out in more detail. I recruited 31 white men who self-report having no hair
by function_seven 9y ago
You had to introduce a lot of assumptions to invalidate the n=31. So let me lay it out in more detail.
I recruited 31 white men who self-report having no hair on at least 500cm^2 of their head. They all had hair as adolescents, and lost it gradually throughout their 20s or early 30s. They're all at least 37 now.
I then sort them into the two groups by assigning sequential IDs in random order. Those with an odd ID are in Group A, those with an even ID are in Group B.
I then give each participant in both groups a placebo for 24 weeks, having them check in every 2 weeks to measure hair growth, if any. At 24 weeks, I randomly assign two new courses of treatment to each group. I do this blindly, giving Group A pill X and Group B pill Y. Another 24 weeks (with check-ins every 2 weeks) and I find that Group A (taking pill X) all have full heads of hair. Well, except for Paul and Maury.
Meanwhile, Tom, in Group B (who took pill Y), is the only member of his group to have a full head of hair.
Statistically-speaking, my n=31 was a great sample size, and I'm also pretty sure Tom discovered he had sugar pills and stole the real stuff from Paul or Maury. :)
To address the possibility of seeing these results due to a confounding variable (like above average testosterone), this is about as likely as shuffling a deck of 31 cards, and all the red ones ending up in the front and all the black ones in the back. About 0.0000003%
- jcranberry 9y agoAh, but you did not precisely define the area in cm^2 of hair which constitutes a full head of hair vs bald!???!? Clearly this is a sham and you needed at least 31^47 participants in this study you charlatan!
- function_seven 9y agoShit. I just rechecked my data, and it appears the "full-head-of-hair" group still had 482cm² of shiny scalp. I need to fire my measurement tech and issue a recall...
- carlmr 9y agoThe point is though, that if enough people do the same study, and only the positive results are published, you're increasing the odds a lot of getting a positive result from someone. That's why companies often finance studies in their drugs. If enough people study their drugs, something which doesn't have a measureable effect, might have a measureable effect in some of these studies. At which sample size this becomes reasonable is a good question though.
- sov 9y agoHow many studies, on average, does it take to show an effect p<=0.05 for n=31 where none exists? How many for n=200?