2022/05/16 by Graham Elliott, Elliott, Graham, Nikolay Kudrin +3
Computer Science · #Econometrics (econ.EM) #FOS: Economics and business #Information and Cyber Security
paper · pdf · doi:10.48550/arxiv.2205.07950
openalex publication_date 2022/05/16 · openalex created_date 2022/05/22 · openalex updated_date 2026/07/28
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.