This chapter examines and searches for evidence of fraud in two clinical data sets from a highly publicized case of scientific misconduct. In this case, data were falsified by Eric Poehlman, a faculty member at the University of Vermont, who pleaded guilty to fabricating more than a decade of data, some connected to federal grants from the National Institutes of Health. Poehlman had authored influential studies on many topics; including obesity, menopause, lipids, and aging. The chapter's classical Benford analysis along with a presentation of a more general class of Benford-like distributions highlights interesting insights into this and similar cases. In addition, this chapter demonstrates how information-theoretic methods and other data-adaptive methods are promising tools for generating benchmark distributions of first significant digits (FSDs) and examining data sets for departures from expectations.
Information-theoretic methods for studying population codes
