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Detecting p-hacking

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  • Graham Elliott
  • Nikolay Kudrin
  • Kaspar Wuthrich

Abstract

We theoretically analyze the problem of testing for $p$-hacking based on distributions of $p$-values across multiple studies. We provide general results for when such distributions have testable restrictions (are non-increasing) under the null of no $p$-hacking. We find novel additional testable restrictions for $p$-values based on $t$-tests. Specifically, the shape of the power functions results in both complete monotonicity as well as bounds on the distribution of $p$-values. These testable restrictions result in more powerful tests for the null hypothesis of no $p$-hacking. When there is also publication bias, our tests are joint tests for $p$-hacking and publication bias. A reanalysis of two prominent datasets shows the usefulness of our new tests.

Suggested Citation

  • Graham Elliott & Nikolay Kudrin & Kaspar Wuthrich, 2019. "Detecting p-hacking," Papers 1906.06711, arXiv.org, revised May 2021.
  • Handle: RePEc:arx:papers:1906.06711
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    Cited by:

    1. Guido W. Imbens, 2021. "Statistical Significance, p-Values, and the Reporting of Uncertainty," Journal of Economic Perspectives, American Economic Association, vol. 35(3), pages 157-174, Summer.

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