P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016)

p -curve, the distribution of significant p -values, can be analyzed to assess if the findings have evidential value, whether p -hacking and file-drawering can be ruled out as the sole explanations for them. Bruns and Ioannidis (2016) have proposed p -curve cannot examine evidential value with observational data. Their discussion confuses false-positive findings with confounded ones, failing to distinguish correlation from causation. We demonstrate this important distinction by showing that a confounded but real, hence replicable association, gun ownership and number of sexual partners, leads to a right-skewed p -curve, while a false-positive one, respondent ID number and trust in the supreme court, leads to a flat p -curve. P -curve can distinguish between replicable and non-replicable findings. The observational nature of the data is not consequential.