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Perspect Psychol Sci. 2014 Nov;9(6):666-81. doi: 10.1177/1745691614553988.

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

Author information

1
University of Pennsylvania uws@wharton.upenn.edu.
2
University of California, Berkeley.
3
University of Pennsylvania.

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.

KEYWORDS:

p-curve; p-hacking; publication bias

PMID:
26186117
DOI:
10.1177/1745691614553988
[Indexed for MEDLINE]

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