Expert: Biden win ‘suspicious,’ 289,000 election-changing ‘excess’ votes
by Paul Bedard, Washington Secrets Columnist | December 30, 2020
An analysis of the 2020 presidential vote suggests that there were 289,000 “excess” votes for Joe Biden in states his victory over President Trump was small, and that differences in votes by neighbors were “suspicious.”
The study by economist John R. Lott Jr., noted for his statistical analysis of guns in America, called into question the victories declared for Biden in Pennsylvania and Georgia and cast a cloud over those in Nevada, Arizona, Michigan, and Wisconsin.
A Simple Test for the Extent of Vote Fraud with Absentee Ballots in the 2020 Presidential Election: Georgia and Pennsylvania Datahttps://t.co/mC2T5HSBDO#AbsenteeBallots #AbsenteeVotes #VoteFraud
— John R Lott Jr. (@JohnRLottJr) December 29, 2020
“The precinct level estimates for Georgia and Pennsylvania indicate that vote fraud may account for Biden’s win in both states. The voter turnout rate data also indicates that there are significant excess votes in Arizona, Michigan, Nevada, and Wisconsin as well,” wrote Lott in his report tweeted earlier today by Trump.
Lott, quoted often by Secrets on gun issues, and recently named a senior adviser for research and statistics at the Office of Justice Programs, presented his report as an independent model that shows the potential for fraud and other voting problems that should be considered as courts sort through suits challenging the election results.
New Lott study estimates 11,350 absentee votes lost to Trump in Georgia. Another 289,000 “excess (fraudulent) votes” across GA, AZ, MI, NV, PA, and WI. Check it out! ➡️https://t.co/eIBjdmox5R pic.twitter.com/2MlKVbBoLO
— Donald J. Trump (@realDonaldTrump) December 30, 2020
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He noted that Trump’s efforts to prove fraud are being stymied by courts demanding proof but not giving him time to find it, the typical discovery phase in fraud cases. His modeling makes a case that there are tons of it, enough for courts to act.
Lott analyzed the voting in two key counties, Fulton County, Georgia, and Allegheny County, Pennsylvania, and found irregularities when compared to the 2016 vote. One key difference was the difference in voting by people in neighboring counties. He found statistically significant differences “just across the street,” suggesting potential vote tampering.
In Fulton County, for example, where the campaign has alleged voting fraud, he found that Trump’s rate of absentee voting was much lower than in the four neighboring counties in the 2020 election versus the 2016 election.
He wrote: “Trump’s percentage of absentee votes was now lower in Fulton county border precincts than in the precincts just across the street in neighboring counties. Trump’s share was 7.19 percentage points lower on the Fulton county side, and the difference was also statistically significant at the 7% level for a two-tailed t-test.”
“This is not likely to have been caused by the general shift to absentee voting among Democrats, because the study controlled for in-person voting. In layman’s terms, in precincts with alleged fraud, Trump’s proportion of absentee votes was depressed — even when such precincts had similar in-person Trump vote shares to their surrounding countries. The fact that the shift happens only in absentee ballots, and when a country line is crossed, is suspicious.”
His analysis suggested that there were 8,280 Biden votes in question. Using a similar analysis of DeKalb County, Georgia, another county where there are concerns, votes, there was the potential for enough fraudulent Biden votes (15,762) to overturn Biden’s win by 12,670 votes certified in a recount.
He found similar results in Pennsylvania.
And his modeling suggested similar findings in Arizona, Michigan, Nevada, and Wisconsin, potentially throwing the election to Trump.
“The estimates here indicate that there were 70,000 to 79,000 ‘excess’ votes in Georgia and Pennsylvania. Adding Arizona, Michigan, Nevada, and Wisconsin, the total increases to up to 289,000 excess votes,” wrote Lott.
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