University of Pennsylvania - The Wharton School
November 13, 2012
I argue that journals should require authors to post the raw data supporting their published results. I illustrate some of the benefits of doing so by describing two cases of fraud I identified exclusively through statistical analysis of reported means and standard deviations. Analyses of the raw data provided important 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. If we want to reduce fraud, we need to require authors to post their raw data.
Number of Pages in PDF File: 32
Keywords: Data transparency, fake data, science, judgment and decision makingworking papers series