title: Just post it: the lesson from two cases of fabricated data detected by statistics alone.
abstract: I argue that requiring authors to post the raw data supporting their published results has the benefit, among many others, of making fraud much less likely to go undetected. I illustrate this point by describing two cases of suspected fraud I identified exclusively through statistical analysis of reported means and standard deviations. Analyses of the raw data behind these published results provided invaluable confirmation of the initial suspicions, ruling out benign explanations (e.g., reporting errors, unusual distributions), identifying additional signs of fabrication, and also ruling out one of the suspected fraud's explanations for his anomalous results. If journals, granting agencies, universities, or other entities overseeing research promoted or required data posting, it seems inevitable that fraud would be reduced.
authors: The Wharton School, University of Pennsylvania.
authors: Uri
authors: U
authors: Simonsohn
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D033181:Information Dissemination / Q000941:ethics; D011584:Psychology / Q000941:ethics*; D011643:Publishing / Q000941:ethics*; D012106:Research; D015871:Scientific Misconduct / Q000941:ethics*; D014495:Universities / Q000941:ethics
keywords: data posting; data sharing; decision making; fake data; judgment; scientific communication