P-Curve: A Key to the File-Drawer

被引:946
作者
Simonsohn, Uri [1 ]
Nelson, Leif D. [2 ]
Simmons, Joseph P. [1 ]
机构
[1] Univ Penn, Wharton Sch, Philadelphia, PA 19104 USA
[2] Univ Calif Berkeley, Haas Sch Business, Berkeley, CA 94720 USA
关键词
publication bias; selective reporting; p-hacking; false-positive psychology; hypothesis testing; PUBLICATION BIAS; PSYCHOLOGICAL-RESEARCH; FUNNEL-PLOT; DECISIONS; REALITY; FOLLOW; VALUES; TESTS; POWER; TRIM;
D O I
10.1037/a0033242
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
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.
引用
收藏
页码:534 / 547
页数:14
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