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.
