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

PLoS One

Uri Simonsohn¹  Leif D. Nelson²  Joseph P. Simmons³ 

[1]Behavioral Science, ESADE Business School, Ramon Llull University, Barcelona, Spain

[2]Marketing Department, Haas School of Business, University of California-Berkeley, Berkeley, California, United States of America

[3]Operations, Information and Decisions department, The Wharton School, University of Pennsylvania, Pennsylvania, United States of America

[4]Public Library of Science, UNITED KINGDOM

DOI  :  10.1371/journal.pone.0213454

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

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.

【 授权许可】

CC BY