Scatter and regularity imply Benford's law... and more

TL;DR

This paper demonstrates that the combination of scatter and regularity in a random variable explains its adherence to Benford's law, providing a simple, general framework that extends beyond traditional log-based explanations.

Contribution

It proves that scattered and regular random variables approximately follow Benford's law, offering a simpler, more general explanation independent of log-specific properties.

Findings

Scattered and regular variables tend to follow Benford's law.

The proofs use simple mathematical tools, making the analysis accessible.

Testing confirms the general version of Benford's law aligns with observed data.

Abstract

A random variable (r.v.) X is said to follow Benford's law if log(X) is uniform mod 1. Many experimental data sets prove to follow an approximate version of it, and so do many mathematical series and continuous random variables. This phenomenon received some interest, and several explanations have been put forward. Most of them focus on specific data, depending on strong assumptions, often linked with the log function. Some authors hinted - implicitly - that the two most important characteristics of a random variable when it comes to Benford are regularity and scatter. In a first part, we prove two theorems, making up a formal version of this intuition: scattered and regular r.v.'s do approximately follow Benford's law. The proofs only need simple mathematical tools, making the analysis easy. Previous explanations thus become corollaries of a more general and simpler one. These results suggest that Benford's law does not depend on properties linked with the log function. We thus propose and test a general version of the Benford's law. The success of these tests may be viewed as an a posteriori validation of the analysis formulated in the first part.