https://en.wikipedia.org/wiki/Benford%27s_law=

Still questioning #electionIntegrity around #election2020 or even #election2022? #Antiscience Hooey.

To all at #FoxNews and all the other Trumpian #socialmedia acolytes still using doubt and fear for political or financial advantage, while pushing us toward civil war: show us the #dataforensics #electionforensics' #datascience or stfu.

Here a blurb on #electiondata from wikipedia on #BenfordsLaw to tell you where you need to look.

#GeorgeSantos: I ate at the Italian restaurant where the #Republican congressman often spends exactly $199.99.

https://slate.com/news-and-politics/2023/01/george-santos-il-bacco-campaign-spending-new-york.html

See also #BenfordsLaw https://en.wikipedia.org/wiki/Benford%27s_law

#uspol

Benford's Law turns out to be a poor test for election fraud

"Why do Biden's votes not follow Benford's Law?"

https://youtube.com/watch?v=etx0k1nLn78

The distribution of values fails to meet the multiple orders of magnitude requirement for applying Benford's Law.

"Benford's Law and the Detection of Election Fraud"

Abstract: The proliferation of elections in even those states that are arguably anything but democratic has given rise to a focused interest on developing methods for detecting fraud in the official statistics of a state's election returns. Among these efforts are those that employ Benford's Law, with the most common application being an attempt to proclaim some election or another fraud free or replete with fraud. This essay, however, argues that, despite its apparent utility in looking at other phenomena, Benford's Law is problematical at best as a forensic tool when applied to elections....

https://www.cambridge.org/core/journals/political-analysis/article/benfords-law-and-the-detection-of-election-fraud/3B1D64E822371C461AF3C61CE91AAF6D

#ForensicAccounting #FraudDetection #BenfordsLaw #elections

For a simple #BenfordsLaw like analysis of election data, the logarithmic distribution of the total vote counts must be wide, and the folded distribution nearly uniform. The #Chicago per-ward data in the #USPresidentialElection has a very narrow log10 distribution: 50% from 400 to 620 votes. The mean is 10^2.7 = 490. Biden should typically get about 0.82*490 = 400 votes. A first-digit peak of 3s and 4s for Biden is statistically reasonable.

https://chicagoelections.gov/en/election-results-specifics.asp

For a simple #BenfordsLaw like analysis of election data, the logarithmic distribution of the total vote counts must be wide; and the folded distribution nearly uniform. The #Milwaukee per-ward data #USPresidentialElection has a narrow log10 distbn: 50% from 570 to 1200 votes. Mean: 10^2.9 = 800. Biden should get about 0.7*800 = 560 votes. A first-digit peak of 5s for Biden is statistically reasonable.

http://web.archive.org/web/20201108052246/https://county.milwaukee.gov/EN/County-Clerk/Off-Nav/Election-Results/Election-Results-Fall-2020

Biden: https://archive.today/2qWYe
Trump: https://archive.today/hkWci