The Power of Tests for Detecting $p$-Hacking

Fuente: arXiv
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Autores principales: Elliott, Graham, Kudrin, Nikolay, Wüthrich, Kaspar
Formato: Preprint
Publicado: 2022
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author Elliott, Graham
Kudrin, Nikolay
Wüthrich, Kaspar
author_facet Elliott, Graham
Kudrin, Nikolay
Wüthrich, Kaspar
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2205_07950
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle The Power of Tests for Detecting $p$-Hacking
Elliott, Graham
Kudrin, Nikolay
Wüthrich, Kaspar
Econometrics
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.
title The Power of Tests for Detecting $p$-Hacking
topic Econometrics
url https://arxiv.org/abs/2205.07950