Minimal Dataset Size For Benford’s Law Application

Abstract Benford’s law describes the effect of specific first significant digit probability distribution in natural datasets. In the case of non-natural or artificial intervention within such datasets, the first digit probability distribution tends to deviate from the theoretical distribution. Benford’s law-based methods are useful in detecting unnatural changes in datasets indicating artificial manipulation of the original data. In our article, we first shortly describe the theory behind this law with the overview of Benford’s law properties. Then we focus on conformity tests for Benford’s law as methods for data change detection com-pared with original dataset. In our research, the datasets were collected from electricity consumption metering devices. We provide the results of conformity with Benford’s law for affected datasets within a series of simulations with extremely small datasets. This size of datasets violates some of the standard conformity rules for Benford’s law.