p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results

U. Simonsohn, Leif D. Nelson, Joseph P. Simmons

Research output: Indexed journal article Articlepeer-review

503 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)666-681
Number of pages16
JournalPerspectives on Psychological Science
Volume9
Issue number6
DOIs
Publication statusPublished - 24 Nov 2014
Externally publishedYes

Keywords

  • p-curve
  • p-hacking
  • publication bias

Fingerprint

Dive into the research topics of 'p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results'. Together they form a unique fingerprint.

Cite this