Journal of Experimental Psychology: General, Forthcoming
14 Pages Posted: 24 Apr 2013 Last revised: 8 Jul 2014
Date Written: April 24, 2013
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.
Keywords: p-hacking, publication bias, file drawer, science, statistics, judgment, decision making
Suggested Citation: Suggested Citation
Simonsohn, Uri and Nelson, Leif D. and Simmons, Joseph P., P-Curve: A Key to the File Drawer (April 24, 2013). Journal of Experimental Psychology: General, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2256237