Do Preregistration and Preanalysis Plans Reduce <i>p</i>-Hacking and Publication Bias? Evidence from 15,992 Test Statistics and Suggestions for Improvement
Abel Brodeur, Nikolai Cook, Jonathan Hartley, Anthony Heyes
Abstract
Preregistration is regarded as an important contributor to research credibility. We investigate this by analyzing the pattern of test statistics from the universe of randomized controlled trial studies published in 15 leading economics journals. We draw two conclusions: (a) Preregistration frequently does not involve a preanalysis plan (PAP), or sufficient detail to constrain meaningfully the actions and decisions of researchers after data are collected. Consistent with this, we find no evidence that preregistration in itself reduces p-hacking and publication bias. (b) When preregistration is accompanied by a PAP we find evidence consistent with both reduced p-hacking and reduced publication bias.