Benford's Law and Fraud Detection. Facts and Legends
Is Benford's law a good instrument to detect fraud in reports of statistical and scientific data? For a valid test the probability of "false positives" and "false negatives" has to be low. However, it is very doubtful whether the Benford distribution is an appropriate tool to discriminate between manipulated and non-manipulated estimates. Further research should focus more on the validity of the test and test results should be interpreted more carefully.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Andreas Diekmann, 2007.
"Not the First Digit! Using Benford's Law to Detect Fraudulent Scientif ic Data,"
Journal of Applied Statistics,
Taylor & Francis Journals, vol. 34(3), pages 321-329.
- Andreas Diekmann, 2005. "Not the First Digit! Using Benford’s Law to Detect Fraudulent Scientific Data," Others 0507001, EconWPA.
- Dewald, William G & Thursby, Jerry G & Anderson, Richard G, 1986. "Replication in Empirical Economics: The Journal of Money, Credit and Banking Project," American Economic Review, American Economic Association, vol. 76(4), pages 587-603, September.
- Andreas Diekmann, 2002. "Diagnose von Fehlerquellen und methodische Qualität in der sozialwissenschaftlichen Forschung [Sources of Bias and Quality of Data in Social Science Research]," ITA manu:scripts 02_04, Institute of Technology Assessment (ITA).
When requesting a correction, please mention this item's handle: RePEc:ets:wpaper:8. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Heidi Bruderer)
If references are entirely missing, you can add them using this form.