P-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results
16 Pages Posted: 11 Jan 2014 Last revised: 19 Nov 2014
Date Written: April 27, 2014
Journals tend to publish only statistically significant evidence, creating a scientific record that markedly overstates the size of effects. We provide a new tool that corrects for this bias without requiring access to nonsignificant results. It capitalizes on the fact that the distribution of significant p-values, p-curve, is a function of the true underlying effect. Researchers armed only with sample sizes and test results of the published findings can correct for publication bias. We validate the technique with simulations and by re-analyzing data from the Many-Labs Replication project. We demonstrate that p-curve can arrive at conclusions opposite that of existing tools by re-analyzing the meta-analysis of the “choice overload” literature.
Keywords: Publication bias, p-curve
Suggested Citation: Suggested Citation