> assume there was no p-hacking Agree that this is definitely an assumption one needs to make, could easily be that BDNF was one variable among many unreported

> assume there was no p-hacking Agree that this is definitely an assumption one needs to make, could easily be that BDNF was one variable among many unreported ones, and this case would be consistent with the other outcome variables in the paper so seems plausible. > this type of result implies that statistical power is tiny, Yes, definitely, BUT the effect in question is an interaction effect so yeah, power's just going to be small from the nature of the design. I was definitely thinking that you'd be looking at a follow up study of the size of multiple hundreds to confirm something like this. I'm realizing that thinking this is a trivial follow-up is is the difference between someone actually might work on real experiments and someone who just works with the numbers. Just want to re-emphasize though that the thing which makes me give this result (some) credence (assuming it's not a desk drawer p-hack) is just the distributions of the observation variable for the two treatment groups. Like even if the means of the BDNF increase are equal between the two arms of the trial, and this p-value is a false pos (which as you say, seems very possible), there's still clearly some other differences between the groups. I strongly suspect a quantile regression on the p50 or p75, rather than an ANOVA on the means, would show a 'more significant' effect; heck even just a log-linear model or something seems like it would be an improvement since there's clearly some skew in the 'Dance' population.