Not the First Digit!: Using Benford's Law to Detect Fraudulent Scientific Data

Abstract

Digits in statistical data produced by natural or social processes are often distributed in a manner described by 'Benford's law'. Recently, a test against this distribution was used to identify fraudulent accounting data. This test is based on the supposition that first, second, third and other digits in real data follow the Benford distribution while the digits in fabricated do not. Is it possible to apply Benford tests to detect fabricated or falsified data as well as fraudulent financial data? We approached this question in two ways. First we examined the use of the Benford distributions as a standard by checking frequencies of the nine possible first and ten possible second digits in published statistical estimates. Second, we conducted experiments in which subjects were asked to fabricate statistical estimates (regression coefficients). The digits in these experimental data were scrutinized for possible deviations from the Benford distribution.