Stats 5(3), pp. 841–855.
ISSN/ISBN: Not available at this time. DOI: 10.3390/stats5030049
Abstract: A frequent problem with classic first digit applications of Benford’s law is the law’s inapplicability to clustered data, which becomes especially problematic for analyzing election data. This study offers a novel adaptation of Benford’s law by performing a first digit analysis after converting vote counts from election data to base 3 (referred to throughout the paper as 1-BL 3), spreading out the data and thus rendering the law significantly more useful. We test the efficacy of our approach on synthetic election data using discrete Weibull modeling, finding in many cases that election data often conforms to 1-BL 3. Lastly, we apply 1-BL 3 analysis to selected states from the 2004 US Presidential election to detect potential statistical anomalies.
Bibtex:
@Article{,
AUTHOR = {Anderson, Katherine M. and Dayaratna, Kevin and Gonshorowski, Drew and Miller, Steven J.},
TITLE = {A New Benford Test for Clustered Data with Applications to American Elections},
JOURNAL = {Stats},
VOLUME = {5},
YEAR = {2022},
NUMBER = {3},
PAGES = {841--855},
URL = {https://www.mdpi.com/2571-905X/5/3/49},
ISSN = {2571-905X},
DOI = {10.3390/stats5030049},
}
Reference Type: Journal Article
Subject Area(s): Statistics, Voting Fraud