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Cerqueti, R and Maggi, M (2021)

Data validity and statistical conformity with Benford’s Law

Chaos, Solitons & Fractals 144, p. 110740 .

ISSN/ISBN: Not available at this time. DOI: 10.1016/j.chaos.2021.110740

Abstract: Benford’s Law is a statistical regularity of a large number of datasets; assessing the compliance of a large dataset with the Benford’s Law is a theme of remarkable relevance, mainly for its practical consequences. Such a task can be faced by introducing a statistical distance concept between the empirical distribution of the data and the random variable associated with Benford’s Law. This paper deals with the problem of measuring the compliance of a random variable – which can be seen as describing the empirical distribution of a collection of data – with the Benford’s Law. It proposes a statistical methodology for detecting the critical values related to conformity/nonconformity with Benford’s Law in some well-established cases of statistical distance. The followed approach is grounded on the proper selection of a family of parametric random variables – the lognormal distribution, in our case – and of a reference statistical distance concept – mean absolute deviation. A discussion of the obtained results is carried out on the ground of the existing literature. Moreover, some open problems are also presented.

@article{, title = {Data validity and statistical conformity with Benford’s Law}, journal = {Chaos, Solitons \& Fractals}, volume = {144}, pages = {110740}, year = {2021}, issn = {0960-0779}, doi = {10.1016/j.chaos.2021.110740}, url = {}, author = {Roy Cerqueti and Mario Maggi}, }

Reference Type: Journal Article

Subject Area(s): Probability Theory, Statistics