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. Copyright 2010 The Authors. Journal Compilation Verein für Socialpolitik and Blackwell Publishing Ltd. 2010.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 11 (2010)
Issue (Month): (08)
|Contact details of provider:|| Web page: http://www.blackwellpublishing.com/journal.asp?ref=1465-6485|
More information through EDIRC
|Order Information:||Web: http://www.blackwellpublishing.com/subs.asp?ref=1465-6485|
References listed on IDEAS
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.:
- 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, 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.
- 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:bla:germec:v:11:y:2010:i::p:397-401. 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: (Wiley-Blackwell Digital Licensing)or (Christopher F. Baum)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.