arXiv subjects

Graham Elliott

Publications and source records attributed to Graham Elliott.

2 recordsLinked to original sources

arXiv2022

The Power of Tests for Detecting $p$-Hacking

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.

econ.EM↗

arXiv2019

Detecting p-hacking

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.

econ.EM↗