Speaker: Asaf Weinstein

Abstract: Suppose that you have a copy of a single issue of a medical journal whose policy is to publish 100 findings with p-values<0.05. You take a look at the first article and see a reported p-value of 0.03. What do you make out of this information, being aware of the selection effect and remembering that you only have access to the published articles? A reasonable correction would be to estimate the fraction of nulls among reported findings with p-value<0.03. We discuss ideas for estimating this quantity from only the reported (truncated) p-values. Focusing attention on estimation rather than testing, we propose an empirical Bayes estimator for the mean effect size for the published findings. This can be considered an analogue of Efron’s (2012) methods for the case where only the selected are observed. This is joint work with Amit Meir.