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November 2011 Quick Anomaly Detection by the Newcomb–Benford Law, with Applications to Electoral Processes Data from the USA, Puerto Rico and Venezuela
Luis Pericchi, David Torres
Statist. Sci. 26(4): 502-516 (November 2011). DOI: 10.1214/09-STS296

Abstract

A simple and quick general test to screen for numerical anomalies is presented. It can be applied, for example, to electoral processes, both electronic and manual. It uses vote counts in officially published voting units, which are typically widely available and institutionally backed. The test examines the frequencies of digits on voting counts and rests on the First (NBL1) and Second Digit Newcomb–Benford Law (NBL2), and in a novel generalization of the law under restrictions of the maximum number of voters per unit (RNBL2). We apply the test to the 2004 USA presidential elections, the Puerto Rico (1996, 2000 and 2004) governor elections, the 2004 Venezuelan presidential recall referendum (RRP) and the previous 2000 Venezuelan Presidential election. The NBL2 is compellingly rejected only in the Venezuelan referendum and only for electronic voting units. Our original suggestion on the RRP (Pericchi and Torres, 2004) was criticized by The Carter Center report (2005). Acknowledging this, Mebane (2006) and The Economist (US) (2007) presented voting models and case studies in favor of NBL2. Further evidence is presented here. Moreover, under the RNBL2, Mebane’s voting models are valid under wider conditions. The adequacy of the law is assessed through Bayes Factors (and corrections of p-values) instead of significance testing, since for large sample sizes and fixed α levels the null hypothesis is over rejected. Our tests are extremely simple and can become a standard screening that a fair electoral process should pass.

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Luis Pericchi. David Torres. "Quick Anomaly Detection by the Newcomb–Benford Law, with Applications to Electoral Processes Data from the USA, Puerto Rico and Venezuela." Statist. Sci. 26 (4) 502 - 516, November 2011. https://doi.org/10.1214/09-STS296

Information

Published: November 2011
First available in Project Euclid: 28 February 2012

zbMATH: 1331.62492
MathSciNet: MR2951385
Digital Object Identifier: 10.1214/09-STS296

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.26 • No. 4 • November 2011
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