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Detecting p-Hacking
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AI
DOI：10.3982/ECTA18583.png)
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Abstract
En 中文
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 data sets shows the usefulness of our new tests.
Keywords:
p-values
p-curve
complete monotonicity
publication bias
