ArticlePloS one2016
Assessing Conformance with Benford's Law: Goodness-Of-Fit Tests and Simultaneous Confidence Intervals.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Anderson-Darling and Watson tests for the geometric distribution with estimated probability of success.PloS one · 2024Article
- Testing for Benford's Law in very small samples: Simulation study and a new test proposal.PloS one · 2022Article
- Natural spatial pattern-When mutual socio-geo distances between cities follow Benford's law.PloS one · 2022Article
- Benchmarking of public health surveillance of COVID-19 in Colombia: First semesterBiomedica : revista del Instituto Nacional de Salud · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Benford's Law is a probability distribution for the first significant digits of numbers, for example, the first significant digits of the numbers 871 and 0.22 are 8 and 2 respectively. The law is particularly remarkable because many types of data are considered to be consistent with Benford's Law and scientists and investigators have applied it in diverse areas, for example, diagnostic tests for mathematical models in Biology, Genomics, Neuroscience, image analysis and fraud detection. In this article we present and compare statistically sound methods for assessing conformance of data with Benford's Law, including discrete versions of Cramér-von Mises (CvM) statistical tests and simultaneous confidence intervals. We demonstrate that the common use of many binomial confidence intervals leads to rejection of Benford too often for truly Benford data. Based on our investigation, we recommend that the CvM statistic Ud(2), Pearson's chi-square statistic and 100(1 - α)% Goodman's simultaneous confidence intervals be computed when assessing conformance with Benford's Law. Visual inspection of the data with simultaneous confidence intervals is useful for understanding departures from Benford and the influence of sample size.
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