Date of this Version
Perspectives on Psychological Science
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 reanalyzing data from the Many-Labs Replication project. We demonstrate that p-curve can arrive at conclusions opposite that of existing tools by reanalyzing the meta-analysis of the “choice overload” literature.
publication bias, p-hacking, p-curve
Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014). p-Curve and Effect Size Correcting for Publication Bias Using Only Significant Results. Perspectives on Psychological Science, 9 (6), 666-681. http://dx.doi.org/10.1177/1745691614553988
Date Posted: 27 November 2017
This document has been peer reviewed.