The Power of Tests for Detecting $p$-Hacking

Abstract

A flourishing empirical literature investigates the prevalence of $p$-hacking based on the distribution of $p$-values across studies. Interpreting results in this literature requires a careful understanding of the power of methods for detecting $p$-hacking. We theoretically study the implications of likely forms of $p$-hacking on the distribution of $p$-values to understand the power of tests for detecting it. Power can be low and depends crucially on the $p$-hacking strategy and the distribution of true effects. Combined tests for upper bounds and monotonicity and tests for continuity of the $p$-curve tend to have the highest power for detecting $p$-hacking.