Statistical Fallacies in Claims about “Massive and Widespread Fraud” in the 2020 Presidential Election: Examining Claims Based on Aggregate Election Results

Bernard Grofman and Jonathan Cervas

Statistics and Public Policy, 2024, vol. 11, issue 1, 2289529

Abstract: Years after the election, a substantial portion of the electorate, including a significant majority of Republican voters and numerous Republican officials, continue to believe that the 2020 election was stolen. This essay reviews claims of alleged massive electoral fraud in the 2020 U.S. presidential election. These claims are based on analyses of aggregate-level election data. Although the underlying data in each of the 13 claims we review are accurately described, our review reveals that the interpretations of the election data, which suggest massive fraud, are based on invalid statistical or illogical reasoning. In summary, the conclusions about fraud derived from these statistical analyses are categorically incorrect. We believe this article will serve as a valuable educational tool for the press, the public, and students. It underscores the dangers of misusing statistical inference and emphasizes the importance of accurate statistical analysis in political discourse. By discussing statistical fallacies in a nontechnical manner, we aim to make our critiques accessible to a broad, nonspecialist audience. This significantly contributes to the understanding of misinformation and its impact on democracy and public trust in electoral processes. Supplementary materials for this article are available online.

Date: 2024
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DOI: 10.1080/2330443X.2023.2289529

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