Statistical Aberrations Can Only Be Explained By Cheating

The numbers don’t lie.

Statistical aberrations abound in the 2020 election.

Their only plausible explanation: Unprecedented voter fraud.

There are not just one or two statistical oddities associated with the 2020 Presidential election—there are numerous occurrences.

In fact, it is the sheer size and number of the statistical aberrations that are driving the Trump post-election legal team’s efforts.

In Michigan and Wisconsin during the wee hours of the morning, following the day of the election, the vote count for Joe Biden took a dramatic leap.

The problem: Vertical step functions are statistical impossibilities.

The step functions represent a nearly 100% vote for Joe Biden—according to Benford’s Law this is beyond improbable it’s impossible—without some form of manipulation.

In truth, one does not need to be a statistician or scientist to see a 100% one-sided outcome is unrealistic.

It wasn’t reasonable to the county clerks in Michigan or Wisconsin—it’s why they didn’t add these votes to the totals until the wee hours of the morning (4-5 am). They knew there would be scrutiny—and they were right.

The probable culprit to the 100% rise in Mr. Biden’s vote count: mail-in ballots.

Perhaps the most egregious example of the 100% vote phenomena—came from Pennsylvania—specifically Philadelphia.

Leading by an insurmountable nearly 700,000 votes in Pennsylvania, Trump’s lead evaporated in the wee hours of the morning due to mail-in ballots generated in Philadelphia.

Given the President’s sizable lead, the only way Mr. Biden overtook him was to have nearly 100% of the mail-in ballot go his way.

Mr. Biden could never have overtaken President Trump’s lead if the mail-in ballot had been even a 90/10 split—liberals needed 100% of the mail-in vote.

This is beyond statistical anomaly—it’s a complete aberration.  

The problem for liberals—the sheer one-sidedness and size of the mail-in ballot counts around the country demand attention. (Read: Successful Ballot Harvesting Requires Economies of Scale)

The step function vote count aberration wasn’t the only issue in Michigan.

There was the analysis done by Richard Baris of Big Data Poll, which uncovered evidence of nearly 10,000 dead Michiganders along with 2,000 centenarians casting votes in the 2020 election.

Whether those votes were purged from the vote tallies is unknown—what is known is nearly 12,000 unlikely voters applied for and received mail-in ballots. (Read: Follow the Dead)

To compound matters, analysis done on voting in three large Republican dominated counties in Michigan revealed disturbing results.

Dr. Shiva Ayyadurai, along with other individuals not associated with Dr. Ayyadurai, analyzed three large Republican counties in Michigan (Oakland, Macomb and Kent). What the data revealed was as precincts within these counties became more Republican, Donald Trump’s percentage of the Republican vote went down.

President Trump’s percentage of the Republican vote didn’t remain level, instead it formed a downward regression line.

In fact, curve fitting shows an almost perfect negatively sloped line.

(Note: Curve fitting is a fancy term for drawing a line (or curve) that best tracks distinct data points, with approximately the same number of data points above and below the line (curve).)

First, a perfect fit is never an expected outcome when dealing with data associated with natural events such as voting.

Second, and perhaps more telling, any trend tied to either greater enthusiasm or a decline in enthusiasm for President Trump should have shown itself as a shift in the baseline of President Trump’s vote within the county—i.e. the entire precent of the vote would have been shifted either higher or lower—not produced a negatively slope line.

This is an issue of homogeneous distribution.

Remember the analysis done in Oakland, Macomb and Kent counties only reviewed the vote by Republicans—it was Party-centric.

So, if as speculated by liberals, there was a fleeing from Donald Trump—the percentage of Republican support would have shown a drop across the board and would not be correlated to the percentage of Republicans within a particular precinct.

(Note: There is other antidotal evidence to refute the liberal claim—namely, Donald Trump’s performance across the nation where he outperformed his 2016 results.)

The hard correlation between the percentage of Republicans in a precinct and the percentage of the Republican vote received by Donald Trump—is a statistical improbability.

By Dr. Ayyadurai’s estimates, in just the three counties he analyzed, ~67,000 votes were taken away from President Trump and switched to his opponent—resulting in a net difference of 138,000 votes.

Dr. Ayyadurai’s conclusion: The regression line could have only been the result of sophisticated software embedded in the voting machines.

From the perspective of data analysis—the conclusions reached by Dr. Ayyadurai and others are reasonable. In fact, there are no other more plausible explanations one can offer given the data. (Read: Sweeping the Change—An Elegant Way to Cheat)

There were indeed some unnatural forces at work in the counties analyzed.

The work by Dr. Ayyadurai and others is what is driving the efforts of Trump legal team attorney Sidney Powell.

In a November 19th press conference, Ms. Powell laid out a chilling revelation—they had evidence there was indeed code hidden within the software designed by Smartmatic that did exactly as Dr. Ayyadurai predicted. (Read: Trump’s Legal Team—What’s Their Game Plan (Part II))

It is clear to an objective observer—historic cheating took place in multiple states (if not the entire country—(Read: Chaos Theory Suggests What Happened in One State Happened in Others).

The numbers simply don’t lie.

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