Citation

Simonsohn U, Nelson LD, Simmons JP. PLoS One 2019; 14(3): e0213454.

Affiliation

Operations, Information and Decisions department, The Wharton School, University of Pennsylvania, Pennsylvania, United States of America.

Copyright

(Copyright © 2019, Public Library of Science)

DOI

10.1371/journal.pone.0213454

PMID

30856227

Abstract

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.

Language: en