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Benford's Law: Theory and Applications

Author

Listed:
  • Steven J. Miller

    (Williams College)

Abstract

Benford’s law states that the leading digits of many data sets are not uniformly distributed from one through nine, but rather exhibit a profound bias. This bias is evident in everything from electricity bills and street addresses to stock prices, population numbers, mortality rates, and the lengths of rivers. Here, Steven Miller brings together many of the world’s leading experts on Benford’s law to demonstrate the many useful techniques that arise from the law, show how truly multidisciplinary it is, and encourage collaboration. Beginning with the general theory, the contributors explain the prevalence of the bias, highlighting explanations for when systems should and should not follow Benford’s law and how quickly such behavior sets in. They go on to discuss important applications in disciplines ranging from accounting and economics to psychology and the natural sciences. The contributors describe how Benford’s law has been successfully used to expose fraud in elections, medical tests, tax filings, and financial reports. Additionally, numerous problems, background materials, and technical details are available online to help instructors create courses around the book. Emphasizing common challenges and techniques across the disciplines, this accessible book shows how Benford’s law can serve as a productive meeting ground for researchers and practitioners in diverse fields.

Suggested Citation

  • Steven J. Miller, 2015. "Benford's Law: Theory and Applications," Economics Books, Princeton University Press, edition 1, number 10527.
  • Handle: RePEc:pup:pbooks:10527
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    Citations

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    Cited by:

    1. Roy Cerqueti & Claudio Lupi, 2021. "Some New Tests of Conformity with Benford’s Law," Stats, MDPI, vol. 4(3), pages 1-17, September.
    2. Hsiang-chi Tseng & Wei-neng Huang & Ding-wei Huang, 2017. "Modified Benford’s law for two-exponent distributions," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1403-1413, March.
    3. Gueron, Eduardo & Pellegrini, Jerônimo, 2022. "Application of Benford–Newcomb law with base change to electoral fraud detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    4. Ausloos, Marcel & Ficcadenti, Valerio & Dhesi, Gurjeet & Shakeel, Muhammad, 2021. "Benford’s laws tests on S&P500 daily closing values and the corresponding daily log-returns both point to huge non-conformity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    5. Huang, Yasheng & Niu, Zhiyong & Yang, Clair, 2020. "Testing firm-level data quality in China against Benford’s Law," Economics Letters, Elsevier, vol. 192(C).
    6. Madeleine Farris & Noah Luntzlara & Steven J. Miller & Lily Shao & Mengxi Wang, 2021. "Recurrence relations and Benford’s law," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 797-817, September.
    7. Arno Berger & Theodore P. Hill, 2021. "The mathematics of Benford’s law: a primer," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 779-795, September.
    8. Katherine M. Anderson & Kevin Dayaratna & Drew Gonshorowski & Steven J. Miller, 2022. "A New Benford Test for Clustered Data with Applications to American Elections," Stats, MDPI, vol. 5(3), pages 1-15, August.
    9. Arno Berger & Chuang Xu, 2019. "Best Finite Approximations of Benford’s Law," Journal of Theoretical Probability, Springer, vol. 32(3), pages 1525-1553, September.
    10. Don Lemons & Nathan Lemons & William Peter, 2021. "First Digit Oscillations," Stats, MDPI, vol. 4(3), pages 1-7, July.
    11. Barabesi, Lucio & Pratelli, Luca, 2020. "On the Generalized Benford law," Statistics & Probability Letters, Elsevier, vol. 160(C).
    12. M Lesperance & W J Reed & M A Stephens & C Tsao & B Wilton, 2016. "Assessing Conformance with Benford’s Law: Goodness-Of-Fit Tests and Simultaneous Confidence Intervals," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-20, March.
    13. Lucio Barabesi & Andrea Cerioli & Domenico Perrotta, 2021. "Forum on Benford’s law and statistical methods for the detection of frauds," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 767-778, September.
    14. Jaroslav Petráš & Marek Pavlík & Ján Zbojovský & Ardian Hyseni & Jozef Dudiak, 2023. "Benford’s Law in Electric Distribution Network," Mathematics, MDPI, vol. 11(18), pages 1-27, September.

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