It's easy to assume the worst of researchers, but I think Gelman's 2013 "Garden of Forking Paths"[1] does a good job of showing how it doesn't have to be intent

It's easy to assume the worst of researchers, but I think Gelman's 2013 "Garden of Forking Paths"[1] does a good job of showing how it doesn't have to be intentional misconduct. It more likely or more often comes down to implicitly selecting your analysis plan given the data, and not correcting for the (hidden) multiple comparison problem that arises when doing frequentist statistical tests in that reality: the theory behind null-hypothesis testing and p-values dictates you account for all the other analytical choices you could have made for that data, or would have made in the face of other datasets. Gelman's other point is this is all made much worse in inherently low-power fields like psychology and the social sciences. A field like physics can "get away with" it more because making the appropriate corrections is less likely to wildly change the conclusions. [1]: http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf http://www.stat.columbia.edu/~gelman/research/unpublished/p_...