Significance 13(3) pp. 38-41.
ISSN/ISBN: Not available at this time. DOI: 10.1111/j.1740-9713.2016.00919.x
Abstract: Since the 1990s, a mathematical phenomenon known as Benford’s law has been held aloft as a guard against fraud – as a way to check whether data sets are free from interference. Benford’s law does tell us something interesting about the frequency of leading digits in many natural data sets. But if a data set deviates from Benford’s law, is that evidence that the figures within are fraudulent? Not necessarily. Without an error term (which many articles fail to mention) it is too imprecise to say simply that a data set “does not conform”. To rectify this, this paper presents a concrete, empirical estimate for the phenomenon’s sampling distribution, where it is applicable. Many published test results alleging to have found non-conformance to Benford’s Law in post-hoc examined records, actually report levels of variation that are well within the range of ordinary variation.
Bibtex:
@article {,
AUTHOR = {William M. Goodman},
TITLE = {The promises and pitfalls of Benford's law},
JOURNAL = {Significance},
YEAR = {2016},
VOLUME = {13},
NUMBER = {3},
PAGES = {38--41},
DOI = {10.1111/j.1740-9713.2016.00919.x},
URL = {https://www.researchgate.net/publication/303831266_The_promises_and_pitfalls_of_Benford%27s_law},
}
Reference Type: Journal Article
Subject Area(s): Accounting, Statistics