Joe cheated.
Here's Proof.
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Data scientists bring forward evidence showing the manipulations and vote swings on election night.
(Testimony given before the Georgia State Senate.)
The Initial Indicator: Benford's Law Irregularities in Biden-won Contested States
(written Nov 6th, 2020)
Benford's law states that, in natural data sets, the leading digits are always distributed in a specific, nonuniform way. While one might think that the number 1 would appear as the first digit 11 percent of the time (i.e., one of nine possible numbers), it actually appears about 30 percent of the time (Figure 1). Nine, on the other hand, is the first digit less than 5 percent of the time. The theory covers the first digit, second digit, first two digits, last digit and other combinations of digits because the theory is based on a logarithm of probability of occurrence of digits.
It is commonly used to detect fraud in large data sets by comparing leading digits to what the Benford curve (Figure 1) predicts.
Legal status: In the United States, evidence based on Benford's law has been admitted in criminal cases at the federal, state, and local levels. Evidence for the Enron case hinged on the Benford analysis of their accounting numbers (Figure 2).
Figure 1: Benford's Law
Figure 2: Enron scandal - Benford Analysis
Precedent for elections: Benford's law has been invoked as evidence of fraud in the 2009 Iranian elections. The Benford irregularity in the Iran case was shown through the below distribution graph. Note that the expected Benford distribution is in black, and the Iran distribution is grey.
The slight "unBenfordness" of the above distribution (hint: look at number 7) was enough for major international accusations of election fraud and the demonstrations of millions of Iranians across multiple cities.
US Election Fraud 2020: Now look at the US Election's "unBenfordness" and where they cluster in tight state races where Biden was declared victor (graphs for Chicago, Milwaukee, WI, and Allegheny, PA). (Source) Bonus: A comparison between Biden's Milwaukee numbers and the Enron case is also provided.
As is plain, Benford analysis show multiple issues with the US election numbers that are a lot more blatant than the Iranian election fraud case and even gives the Enron fraud case a run for its money. Biden numbers in contested states are not even close to following the Benford curve.
The usage of Benford in elections has been disputed in the past, but usually for cases like Iran where only one digit falls outside the curve. For the Milwaukee graph alone, five leading digits lie outside the predicted Benford curve.
To put it another way: If Biden didn't commit fraud, Benford's law -- which is also used to detect fraud in taxes, scientific papers, accounting, and census data everywhere in the world -- would be completely disproved.
Here is a full Twitter thread explaining the statistical anomalies of Biden's numbers under statistical analysis.
Statistical Anomalies: "Vote Spikes" Keep Favoring Biden over Trump
An update in Michigan listed as of 6:31AM Eastern Time on November 4th, 2020, which shows 141,258 votes for Joe Biden and 5,968 votes for Donald Trump
An update in Wisconsin listed as 3:42AM Central Time on November 4th, 2020, which shows 143,379 votes for Joe Biden and 25,163 votes for Donald Trump
A vote update in Georgia listed at 1:34AM Eastern Time on November 4th, 2020, which shows 136,155 votes for Joe Biden and 29,115 votes for Donald Trump
An update in Michigan listed as of 3:50AM Eastern Time on November 4th, 2020, which shows 54,497 votes for Joe Biden and 4,718 votes for Donald Trump
Here's an example graph to show what this means:
In the above Wisconsin graph, a single update to the vote count brought Biden from trailing by over 100,000 votes into the lead. Note that the x-axis (time) expressed in Central Standard Time (CST).
A statistician / group of statisticians did an analysis of the above (all data points sourced from scraped New York Times data) and formally proved that the statistical anomalies are enough to warrant investigation towards election fraud. Here's the paper:
https://votepatternanalysis.substack.com/p/voting-anomalies-2020
Here's a summary of their argument:
The basic intuition is: big margins are one thing, and so are super-skewed results, but it’s weird to have them both at the same time, as they generally become inversely related as either value increases.
We will demonstrate below that the data overwhelmingly follow this intuition, but that four key vote updates identified by this report cut against this intuition.
In particular, we will show the existence of a very strong inverse relationship within vote updates, across all states and times, between the difference of votes for Joe Biden and Donald Trump (often referred to as the “Biden-Trump margin”) and the the ratio of Joe Biden’s votes to Donald Trump’s votes (often referred to as the “Biden:Trump ratio”).
Here at JoeCheated.com we are huge fans of this work, so we will not take traffic away from them by giving you the complete explanation on our site. Please go to their site and read through the complete arguments yourself.
Ghosts in the Machine: Dead Voters in the Voter Roll
Written Jan 6th, 2020
The following spreadsheet contains a list of over 300 dead people who registered to vote in Detroit in the 2020 US Election. You are welcome to verify each name on this list through this public link: https://mvic.sos.state.mi.us/Voter/Index
(if you don't see the spreadsheet, click here)
Demonstration: Here are screenshots of Twitter user Fleccas showing that a 118 year old “William Bradley”registered to vote and then actually voted via absentee ballot in Wayne County, Michigan.
William Bradley died in 1984.
Here is a walkthrough showing how you can find dead voters (i.e., people born in the 1800s) directly on the Pennsylvania voter list, through Open Data Pennsylvania. (Video Source.)
PAVideoVoterFraud.mp49 Reasons why the 2020 Presidential Election is Deeply Puzzling (From Spectator.com)
Full article here: https://spectator.us/reasons-why-the-2020-presidential-election-is-deeply-puzzling/
Late on election night, with Trump comfortably ahead, many swing states stopped counting ballots. In most cases, observers were removed from the counting facilities. Counting generally continued without the observers.
Statistically abnormal vote counts were the new normal when counting resumed. They were unusually large in size (hundreds of thousands) and had an unusually high (90 percent and above) Biden-to-Trump ratio.
Late arriving ballots were counted. In Pennsylvania, 23,000 absentee ballots have impossible postal return dates and another 86,000 have such extraordinary return dates they raise serious questions
The failure to match signatures on mail-in ballots. The destruction of mail in ballot envelopes, which must contain signatures
Historically low absentee ballot rejection rates despite the massive expansion of mail voting. Such is Biden’s narrow margin that, as political analyst Robert Barnes observes, ‘If the states simply imposed the same absentee ballot rejection rate as recent cycles, then Trump wins the election’
Missing votes. In Delaware County, Pennsylvania, 50,000 votes held on 47 USB cards are missing
Non-resident voters. Matt Braynard’s Voter Integrity Project estimates that 20,312 people who no longer met residency requirements cast ballots in Georgia. Biden’s margin is 12,670 votes
Serious ‘chain of custody’ breakdowns. Invalid residential addresses. Record numbers of dead people voting. Ballots in pristine condition without creases, that is, they had not been mailed in envelopes as required by law
Statistical anomalies. In Georgia, Biden overtook Trump with 89 percent of the votes counted. For the next 53 batches of votes counted, Biden led Trump by the same exact 50.05 to 49.95 percent margin in every single batch. It is particularly perplexing that all statistical anomalies and tabulation abnormalities were in Biden’s favor. Whether the cause was simple human error or nefarious activity, or a combination, clearly something peculiar happened.
We are huge fans of Patrick Basham, the Director of the Democracy Institute, who wrote this article, and we encourage you to go directly to the article and share it around.
Software "Glitches" that Favor Joe Biden
A glitch in software (Dominion Democracy Suite, brochure here) used to tabulate ballots in Antrim County, Michigan caused at least 6,000 Republican votes to be counted as Democrat, according to Michigan GOP Chairwoman Laura Cox. The miscalculation, Cox said in a press conference, was first reported by a county clerk. A short investigation revealed that 47 counties in Michigan may have also suffered from a similar glitch with the same software, which could have caused some counties to rake in a higher number of Democrat votes than usual.
SoftwareGlitchForBiden.mp4More here:
6,000 might not sound like much, but if we take all of the counties in Michigan that used the Dominion software and multiply it by the number of ballots that switched from Biden to Trump in just that one county, we get:
(1+47) x 6,000 = 288,000
Joe's margin in Michigan is only about 146k.
The Dominion Democracy Suite software is used in 30 states. Google it.
Ghost Voters: 353 U.S Counties had 1.8 Million More Registered Voters than Eligible Citizens
A September 2020 study revealed that 353 U.S. counties had 1.8 million more registered voters than eligible voting-age citizens. In other words, the registration rates of those counties exceeded 100% of eligible voters. The study found eight states showing state-wide registration rates exceeding 100%: Alaska, Colorado, Maine, Maryland, Michigan, New Jersey, Rhode Island, and Vermont.
The study collected the most recent registration data posted online by the states themselves. This data was then compared to the Census Bureau’s most recent five-year population estimates, gathered by the American Community Survey (ACS) from 2014 through 2018. ACS surveys are sent to 3.5 million addresses each month, and its five-year estimates are considered to be the most reliable estimates outside of the decennial census. (Source.)