Just Post It: The Lesson from Two Cases of Fabricated Data Detected by Statistics Alone
University of Pennsylvania - The Wharton School
January 29, 2013
I argue that requiring authors to post the raw data supporting their published results has, among many other benefits, that of making fraud much less likely to go undetected. I illustrate this point by describing two cases of fraud I identified exclusively through statistical analysis of reported means and standard deviations. Analyses of the raw data behind these 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 fraudster’s explanations for his anomalous results.
Number of Pages in PDF File: 31
Keywords: Data transparency, fake data, science, judgment and decision makingworking papers series
Date posted: July 22, 2012 ; Last revised: January 31, 2013
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