This paper studies the effects of p-hacking on the bias of published estimates when papers with statistically significant results are selectively published. We show that fast p-hacking---actions that lead to large changes in p-values---always exacerbates the bias from selective publication. On the other hand, slow p-hacking---actions that lead to small changes in p-values---exacerbates bias when selection is weak, but mitigates it when selection is strong. In a model featuring both types of p-hacking, we show that a normality assumption identifies the true distribution of effects as well as the counterfactual mean that would obtain under selective publication without p-hacking. Applying the model to meta-analyses on the effects of behavioral nudges and development aid, we find suggestive evidence that both mitigation and exacerbation can arise in practice.
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