P-Curve: A Key to the File Drawer
University of Pennsylvania - The Wharton School
Leif D. Nelson
University of California, Berkeley - Haas School of Business
Joseph P. Simmons
University of Pennsylvania - The Wharton School; University of Pennsylvania - Operations & Information Management Department
April 24, 2013
Journal of Experimental Psychology: General, Forthcoming
Because scientists tend to report only studies (publication bias) or analyses (p-hacking) that “work”, readers must ask, “Are these effects true, or do they merely reflect selective reporting?” We introduce p-curve as a way to answer this question. P-curve is the distribution of statistically significant p-values for a set of studies (ps < .05). Because only true effects are expected to generate right-skewed p-curves – containing more low (.01s) than high (.04s) significant p-values – only right-skewed p-curves are diagnostic of evidential value. By telling us whether we can rule out selective reporting as the sole explanation for a set of findings, p-curve offers a solution to the age-old inferential problems caused by file-drawers of failed studies and analyses.
Number of Pages in PDF File: 14
Keywords: p-hacking, publication bias, file drawer, science, statistics, judgment, decision making
Date posted: April 24, 2013 ; Last revised: July 8, 2014
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