Better P-Curves: Making p-curve analysis more robust to errors, fraud, and ambitious p-hacking, a reply to Ulrich and Miller (2015)

Uri Simonsohn, Joseph P. Simmons, Leif D. Nelson

Research output: Indexed journal article Articlepeer-review

192 Citations (Scopus)

Abstract

When studies examine true effects, they generate right-skewed p-curves, distributions of statistically significant results ith more low (.01 s) than high (.04 s) p values. What else can cause a right-skewed p-curve? First, we consider the possibility hat researchers report only the smallest significant p value (as conjectured by Ulrich & Miller, 2015), concluding that it is a ery uncommon problem. We then consider more common problems, including (a) p-curvers selecting the wrong p values, (b) fake data, c) honest errors, and (d) ambitiously p-hacked (beyond p = .05) results. We evaluate the impact of these common problems on the alidity of p-curve analysis, and provide practical solutions that substantially increase its robustness.

Original languageEnglish
Pages (from-to)1146-1152
Number of pages7
JournalJournal of Experimental Psychology: General
Volume144
Issue number6
DOIs
Publication statusPublished - 1 Dec 2015
Externally publishedYes

Keywords

  • Publication bias
  • p-Curve
  • p-Hacking

Fingerprint

Dive into the research topics of 'Better P-Curves: Making p-curve analysis more robust to errors, fraud, and ambitious p-hacking, a reply to Ulrich and Miller (2015)'. Together they form a unique fingerprint.

Cite this