IDEAS home Printed from https://ideas.repec.org/a/taf/usppxx/v11y2024i1p2289529.html
   My bibliography  Save this article

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

Author

Listed:
  • Bernard Grofman
  • Jonathan Cervas

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.

Suggested Citation

  • Bernard Grofman & Jonathan Cervas, 2024. "Statistical Fallacies in Claims about “Massive and Widespread Fraud” in the 2020 Presidential Election: Examining Claims Based on Aggregate Election Results," Statistics and Public Policy, Taylor & Francis Journals, vol. 11(1), pages 2289529-228, January.
  • Handle: RePEc:taf:usppxx:v:11:y:2024:i:1:p:2289529
    DOI: 10.1080/2330443X.2023.2289529
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/2330443X.2023.2289529
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/2330443X.2023.2289529?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:usppxx:v:11:y:2024:i:1:p:2289529. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uspp .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.