title: P-curve: a key to the file-drawer.
abstract: 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.
authors.affiliation: The Wharton School, University of Pennsylvania.
authors.forename: Uri
authors.initials: U
authors.lastname: Simonsohn
authors.affiliation: Haas School of Business, University of California.
authors.forename: Leif D
authors.initials: LD
authors.lastname: Nelson
authors.affiliation: The Wharton School, University of Pennsylvania.
authors.forename: Joseph P
authors.initials: JP
authors.lastname: Simmons
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D011589:Psychology, Experimental; D017594:Publication Bias; D015203:Reproducibility of Results; D013223:Statistics as Topic