P-Curve Won’t Do Your Laundry, But It Will Distinguish Replicable from Non-Replicable Findings in Observational Research: Comment on Bruns & Ioannidis (2016)
Forthcoming, Plos ONE
8 Pages Posted: 14 Dec 2018
Date Written: November 22, 2018
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.
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