Date of this Version
Journal of Experimental Psychology: General
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.
©American Psychological Association, 2014. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available, upon publication, at: http://dx.doi.org/10.1037/a0033242
publication bias, selective reporting, p-hacking, false-positive psychology, hypothesis testing
Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014). P-curve: A Key to The File Drawer. Journal of Experimental Psychology: General, 143 (2), 534-547. http://dx.doi.org/10.1037/a0033242
Date Posted: 27 November 2017
This document has been peer reviewed.