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

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

Producció científica: Article en revista indexadaArticleAvaluat per experts

464 Cites (Scopus)

Resum

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.

Idioma originalAnglès
Pàgines (de-a)666-681
Nombre de pàgines16
RevistaPerspectives on Psychological Science
Volum9
Número6
DOIs
Estat de la publicacióPublicada - 24 de nov. 2014
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