No Evidence for Voter Fraud: A Guide to Statistical Claims About the 2020 Election
Author(s): Andrew C. Eggers University of Chicago
Haritz Garro Stanford University
Justin Grimmer Stanford University
Environment
R
Ubuntu
Packages:
randomForest4 stargazer, tidyverse, readstata
Code
R using RStudio
Data
Precinct level voter data in selected swing staes
Results
What's inside
After the 2020 US presidential election Donald Trump alleged widespread voter fraud. Trump’s supporters deployed several statistical claims that supposedly demonstrated that Joe Biden’s electoral victory in some states, or his popular vote in the country, were fraudulently obtained. Reviewing the most prominent of these statistical claims shows that none of them is even remotely convincing.
The common logic behind these claims is that, if the election were fairly conducted, some feature of the observed 2020 election result would be unlikely or impossible. In each case the purportedly anomalous fact is either not a fact or not anomalous.The Capsule uses Jupyter notebooks and provides tutorials for how Monet can be used to analyze scRNA-Seq data, e.g. for generating simple t-SNE visualizations.
Who uses it
Political scientists, historians, election officials, interested public
Why we like this
The study relies on statistics to disprove statistical claims about widespread voter fraud in the 2020 election.
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