Digital Analysis of Crime Statistics: Does Crime Conform to Benford’s Law?

Hickman, Matthew; Rice, Stephen K.

doi:10.1007/s10940-010-9094-6

Cited by 26 publications

(15 citation statements)

References 38 publications

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“…These include geological streamflow rates [ 14 ], radioactive half-lives [ 15 ], hadron widths [ 16 ], numerous statistical distributions [ 17 , 18 ], auction prices [ 19 ], and business invoices and tax returns [ 8 , 9 , 20 ], as well as the original datasets by studied Benford [ 3 ]. The precision with which various types of data follow Benford’s Law has led to its widespread application for forensic accounting and auditing [ 8 , 21 , 22 ], and while this application represents by far the most active (and commercial) use of the law, there are nonetheless serious practical concerns regarding the validity and justification of this practice [ 9 , 23 ]. Benford’s Law has not yet found robust application in the sciences, although from early on there have been suggestions that it could be used for diagnostic applications in computer design and scientific calculation errors [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks

2015

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Benford’s Law describes the finding that the distribution of leading (or leftmost) digits of innumerable datasets follows a well-defined logarithmic trend, rather than an intuitive uniformity. In practice this means that the most common leading digit is 1, with an expected frequency of 30.1%, and the least common is 9, with an expected frequency of 4.6%. Currently, the most common application of Benford’s Law is in detecting number invention and tampering such as found in accounting-, tax-, and voter-fraud. We demonstrate that answers to end-of-chapter exercises in physics and chemistry textbooks conform to Benford’s Law. Subsequently, we investigate whether this fact can be used to gain advantage over random guessing in multiple-choice tests, and find that while testbank answers in introductory physics closely conform to Benford’s Law, the testbank is nonetheless secure against such a Benford’s attack for banal reasons.

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Section: Introductionmentioning

confidence: 99%

Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks

2015

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“…Numerous empirical data, as economic data ( [55,16]), social data ( [31,42,28]), demographic data ( [48,39]), physical data ( [36,13,47,52,18,3,1]), biological data ( [17,26,37]), data in psychology ( [20,6,15]) or internet data ( [4]), for instance, conform to Benford's law ( [34,46]); to such an extent that this law was used to detect possible frauds in lists of socio-economic data ( [59,45,22,53,58,51,14,38]) or in scientific publications ( [2]).…”

Section: Received: April 28 2020 C 2020 Academic Publicationsmentioning

confidence: 99%

Limits of Benford's Law in Experimental Field

Silva¹

2020

Int. J. of Appl. Math.

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“…See for example: Nigrini (1996Nigrini ( & 1999; Ley (1996); Hill (1998); Geyer & Williamson-Pepple (2004) ;Cho & Gaines (2007) ;Hickman & Rice (2010); & Reddy & Sebastin (2012). Note 2.…”

Section: Notesmentioning

confidence: 99%

Detecting Newcomb-Benford Digital Frequency Anomalies in the Audit Context: Suggested Chi2 Test Possibilities

Lusk

Halperin

2014

AFR

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Digital Frequency Testing [DFT] has achieved justifiable currency as a valuable part of the panoply of the auditor. There are a number of inference models, such as the parametric test for proportional differences to entropic screening, that can be used to create information regarding the use of extended procedures to investigate the difference between the Observed digital frequency compared to the Expectation benchmark. One inferential model which seems ideal for DFT in the audit context is the Chi2 model as it is formed as the ratio of the square of the difference between the Observed and the Expectation benchmarked by the Expectation. This Chi2 ratio fits perfectly the usual audit investigation sensitivity imperative developed by the analytic procedures phase of the audit. However, there are Expectation Specification and Sample Size issues that need to be considered in using the Chi2 test in DFT. We present and illustrate in detail three testing models focusing on the Pearson and the Direct Benchmarking expectation differences with suggestions as to sample size control that could be used by the auditor in the certification audit. We offer important audit action information regarding the inference information that is related to the overall Chi2 test as well as to the Chi2 cell contribution signals. Finally, we have a Decision Support System [DSS] that aides in the creation of this Chi2 audit-screening information. The DSS, programmed in Excel-VBA, is available from the corresponding author as a free download without restriction to its use.

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Digital Analysis of Crime Statistics: Does Crime Conform to Benford’s Law?

Cited by 26 publications

References 38 publications

Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks

Benford’s Law: Textbook Exercises and Multiple-Choice Testbanks

Limits of Benford's Law in Experimental Field

Detecting Newcomb-Benford Digital Frequency Anomalies in the Audit Context: Suggested Chi2 Test Possibilities

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