The Power of Tests for Detecting $p$-Hacking
Graham Elliott (),
Nikolay Kudrin and
Kaspar W\"uthrich
Papers from arXiv.org
Abstract:
$p$-Hacking undermines the validity of empirical studies. 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 depends crucially on the $p$-hacking strategy and the distribution of true effects. Publication bias can enhance the power for testing the joint null of no $p$-hacking and no publication bias.
Date: 2022-05, Revised 2024-04
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2205.07950
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