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Not the First Digit! Using Benford’s Law to Detect Fraudulent Scientific Data

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Author Info

  • Andreas Diekmann

    (ETH Zurich)

Abstract

Digits in statistical data produced by natural or social processes are often distributed in a manner described by “Benford’s law”. Recently, a test against this distribution was used to identify fraudulent accounting data. This test is based on the supposition that real data follow the Benford distribution while fabricated data do not. Is it possible to apply Benford tests to detect fabricated or falsified scientific data as well as fraudulent financial data? We approached this question in two ways. First, we examined the use of the Benford distribution as a standard by checking digit frequencies in published statistical estimates. Second, we conducted experiments in which subjects were asked to fabricate statistical estimates (regression coefficients). These experimental data were scrutinized for possible deviations from the Benford distribution. There were two main findings. First, the digits of the published regression coefficients were approximately Benford distributed. Second, the experimental results yielded new insights into the strengths and weaknesses of Benford tests. At least in the case of regression coefficients, there were indications that checks for digit-preference anomalies should focus less on the first and more on the second and higher-digits.

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File URL: http://128.118.178.162/eps/othr/papers/0507/0507001.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Others with number 0507001.

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Length: 25 pages
Date of creation: 09 Jul 2005
Date of revision:
Handle: RePEc:wpa:wuwpot:0507001

Note: Type of Document - pdf; pages: 25
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Web page: http://128.118.178.162

Related research

Keywords: Benford; Benford's law; falsification of data; fabrication of data;

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References

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  1. Schraepler, Joerg-Peter & Wagner, Gert G., 2003. "Identification, Characteristics and Impact of Faked Interviews in Surveys: An Analysis by Means of Genuine Fakes in the Raw Data of SOEP," IZA Discussion Papers 969, Institute for the Study of Labor (IZA).
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Blog mentions

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  1. Statistical Sleuthing on the Iran Election
    by (author unknown) in The Numbers Guy on 2009-07-01 00:46:58
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Cited by:
  1. Joerg-Peter Schraepler, 2011. "Benford’s Law as an Instrument for Fraud Detection in Surveys Using the Data of the Socio-Economic Panel (SOEP)," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(5-6), pages 685-718, November.
  2. Susumu Shikano & Verena Mack, 2011. "When Does the Second-Digit Benford’s Law-Test Signal an Election Fraud? Facts or Misleading Test Results," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(5-6), pages 719-732, November.
  3. Dlugosz, Stephan & Müller-Funk, Ulrich, 2012. "Ziffernanalyse zur Betrugserkennung in Finanzverwaltungen: Prüfung von Kassenbelegen," Arbeitsberichte des Instituts für Wirtschaftsinformatik 133, University of Münster, Department of Information Systems.
  4. Chase Thiel & Zhanna Bagdasarov & Lauren Harkrider & James Johnson & Michael Mumford, 2012. "Leader Ethical Decision-Making in Organizations: Strategies for Sensemaking," Journal of Business Ethics, Springer, vol. 107(1), pages 49-64, April.
  5. Bruno S. Frey, 2010. "Withering academia?," IEW - Working Papers 512, Institute for Empirical Research in Economics - University of Zurich.
  6. Bernhard Rauch & Max Göttsche & Gernot Brähler & Stefan Engel, 2011. "Fact and Fiction in EU‐Governmental Economic Data," German Economic Review, Verein für Socialpolitik, vol. 12(3), pages 243-255, 08.
  7. Andreas Diekmann & Ben Jann, 2010. "Benford's Law and Fraud Detection: Facts and Legends," German Economic Review, Verein für Socialpolitik, vol. 11, pages 397-401, 08.
  8. Karl-Heinz Tödter, 2009. "Benford's Law as an Indicator of Fraud in Economics," German Economic Review, Verein für Socialpolitik, vol. 10, pages 339-351, 08.
  9. Johannes Bauer & Jochen Gross, 2011. "Difficulties Detecting Fraud? The Use of Benford’s Law on Regression Tables," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(5-6), pages 733-748, November.
  10. Jesus Gonzalez-Garcia & Gonzalo C. Pastor, 2009. "Benford’s Law and Macroeconomic Data Quality," IMF Working Papers 09/10, International Monetary Fund.
  11. Holz, Carsten, 2013. "The Quality of China's GDP Statistics," MPRA Paper 51864, University Library of Munich, Germany.
  12. Lin, Fengyi & Wu, Sheng-Fu, 2014. "Comparison of cosmetic earnings management for the developed markets and emerging markets: Some empirical evidence from the United States and Taiwan," Economic Modelling, Elsevier, vol. 36(C), pages 466-473.
  13. Brähler, Gernot & Bensmann, Markus & Emke, Anna-Lena, 2010. "Der Einsatz mathematisch-statistischer Methoden in der digitalen Betriebsprüfung," Ilmenauer Schriften zur Betriebswirtschaftslehre, Technische Universität Ilmenau, Institut für Betriebswirtschaftslehre, volume 4, number 42010.

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