P-Curve Fixes Publication Bias: Obtaining Unbiased Effect Size Estimates from Published Studies Alone
Leif D. Nelson
University of California, Berkeley - Haas School of Business
University of Pennsylvania - The Wharton School
Joseph P. Simmons
University of Pennsylvania - The Wharton School; University of Pennsylvania - Operations & Information Management Department
January 10, 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 We provide a new tool that arrives at unbiased effect size estimates while fully ignoring the unpublished record. It capitalizes on the fact that the distribution of significant p-values, p-curve, is a function of the true underlying effect. Researchers armed with only the sample sizes and p-values of the published findings can fully correct for publication bias. We demonstrate the use of p-curve by reassessing the evidence for the impact of “choice overload” from the Psychology literature, and the impact of minimum wage on unemployment from the Economics literature.
Number of Pages in PDF File: 17
Keywords: Publication bias, p-curveworking papers series
Date posted: January 11, 2014 ; Last revised: January 16, 2014
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