文章基本信息

标题：Detecting Evidential Value and p-Hacking With the p-Curve Tool
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作者：Edgar Erdfelder ; Daniel W. Heck
期刊名称：Zeitschrift für Psychologie
印刷版ISSN：2190-8370
电子版ISSN：2151-2604
出版年度：2019
卷号：227
期号：4
页码：249-260
DOI：10.1027/2151-2604/a000383
出版社：Hogrefe Publishing
摘要：Simonsohn, Nelson, and Simmons (2014a) proposed p -curve – the distribution of statistically significant p -values for a set of studies – as a tool to assess the evidential value of these studies. They argued that, whereas right-skewed p -curves indicate true underlying effects, left-skewed p -curves indicate selective reporting of significant results when there is no true effect (“ p -hacking”). We first review previous research showing that, in contrast to the first claim, null effects may produce right-skewed p -curves under some conditions. We then question the second claim by showing that not only selective reporting but also selective nonreporting of significant results due to a significant outcome of a more popular alternative test of the same hypothesis may produce left-skewed p -curves, even if all studies reflect true effects. Hence, just as right-skewed p -curves do not necessarily imply evidential value, left-skewed p -curves do not necessarily imply p -hacking and absence of true effects in the studies involved.
关键词：publication bias;p-hacking;false-positive results;p-curve;ANCOVA