Before revision:  Analytical characterizations of the distributions of p-values under the null of no p-hacking and the alternative where there is p-hacking allow us to both analyze the power of existing tests and provide new more powerful statistical tests for p-hacking . Results are extended to practical situations where there is publication bias and when reported p-values are rounded. We also show that tests for p-hacking based on distributions of t-statistics can be problematic and may not control size. Our proposed tests are shown to have good properties in Monte Carlo studies and are applied to two datasets of p-values .
After revision:  Analytical characterizations of the distributions of p-values under the null of no p-hacking and the alternative where there is p-hacking allow us to both analyze the power of existing tests and provide new more powerful statistical tests for p-hacking . A reanalysis of two prominent datasets shows the usefulness of our new tests .