Evidence map›Paper›PMID 40359045›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

Audits of the 2020 American election show an accurate vote count.

Samuel Baltz, Fernanda Gonzalez, Kevin Guo, Jacob Jaffe, Charles Stewart

Abstract readDataset
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Audits of the 2020 American election show an accurate vote count.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Samuel BaltzMassachusetts Institute of Technology, Cambridge, MA 02139.ORCID 0000-0002-4669-7435
Fernanda GonzalezWellesley College, Wellesley, MA 02481.
Kevin GuoMassachusetts Institute of Technology, Cambridge, MA 02139.
Jacob JaffeStanford University, Stanford, CA 94305.
Charles StewartMassachusetts Institute of Technology, Cambridge, MA 02139.ORCID 0000-0003-1428-7308

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After many elections, the accuracy of the vote count is assessed by retabulating a small percentage of ballots. These audits form one of the richest bodies of evidence regarding electoral legitimacy, which is particularly important in democracies where the accuracy of elections has been prominently questioned. In decentralized democracies such as the United States, however, there is tremendous variation in the conduct and reporting of audits, which are never compiled into one place to facilitate precise analysis of their results. Here, we introduce a nation-scale audit result dataset, which we use to estimate the error rate in vote counting during the 2020 U.S. election. The dataset includes all available postelection tabulation audits, spanning 856 regional governments across 27 states, with 71,702,471 individual votes and a further 1,210,528 ballots, including about 6.2% of all votes cast for Donald Trump and 6.9% of those cast for Joe Biden. We find that election audits shifted the net presidential vote count by only about 0.007%, with similarly minuscule errors across all major types of electoral contests. The construction of a nation-scale election audit dataset represents a novel approach to benchmarking electoral legitimacy and provides particularly direct and comprehensive evidence that Americans' votes were counted correctly in 2020.

Indexed as

democracyelection auditselections

Identifiers

PMID40359045
PMCPMC12107110

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.