TY - JOUR PY - 2019// TI - P-curve won't do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016) JO - PLoS one A1 - Simonsohn, Uri A1 - Nelson, Leif D. A1 - Simmons, Joseph P. SP - e0213454 EP - e0213454 VL - 14 IS - 3 N2 - 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

LA - en SN - 1932-6203 UR - http://dx.doi.org/10.1371/journal.pone.0213454 ID - ref1 ER -