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The Power of Tests for Detecting p -Hacking

Graham Elliott¹; Nikolay Kudrin²; Kaspar Wüthrich³

¹ Department of Economics, UC San Diego [email protected] · ² Department of Economics, Queen's University [email protected] · ³ Department of Economics, University of Michigan [email protected]

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.

DOI

10.1162/rest.a.1688

Pages

1-44

Language

en

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