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New Analysis: Did Biden Actually Win California? Benford’s Law Says No

A new analysis published on American Thinker sheds light on what many people have been wondering: Did Joe Biden actually win the state of California?

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There have been many states whose official results in the 2020 presidential election have been questioned.

Of course, there’s Arizona.

Arizona has been in the middle of a true forensic audit of Maricopa county.

States like Georgia, Michigan, Wisconsin, and Pennsylvania are potentially going to follow in Arizona’s footsteps.

And now a new state could be entering the spotlight of irregular 2020 election results: California.

More and more people are beginning to wonder…

In order to understand the analysis, you first have to understand Benford’s Law.

Prior to the 2020 presidential election, Benford’s Law was widely considered to be the gold standard in identifying fraud.

In fact, Benford’s Law was so trusted that it has been used to uncover financial fraud in businesses.

It is used to identify fraud in numerical data.

So what is it?

According to Towards Data Science:

Benford’s Law, also known as the Law of First Digits or the Phenomenon of Significant Digits, is the finding that the first digits (or numerals to be exact) of the numbers found in series of records of the most varied sources do not display a uniform distribution, but rather are arranged in such a way that the digit “1” is the most frequent, followed by “2”, “3”, and so in a successively decreasing manner down to “9”.

With that in mind, take a look at the following charts:

Of course, Benford’s Law was used to question the accuracy of battleground states.

But hardly anyone used this law in California.

Why?

Because California is a reliably blue state? They usually vote for Democrats.

But could they have actually voted for President Trump in 2020?

That’s what a new analysis from the American Thinker appears to suggest:

The November 3, 2020, Presidential election data from California should set off alarm bells.

Election data generated by the natural voting process will closely match Benford’s Law predictions. (Benford’s Law, or the rule of leading digits, is used as an indicator of fraud in numerical data.) Of course, there can be exceptions, but Benford’s Law points to areas to investigate.

Benford’s Law predicts that for real, natural data, the frequency of leading digits (1 through 9) will occur at or near the following percentages of the transactions or tallies. Deviations from these predicted percentages should be investigated.

Here are the official presidential totals for California, as released by Dr. Shirley N. Weber, Secretary of State (also released by Alex Padilla, Secretary of State through January 28, 2021):

The difference in votes tallied is 5,104,121 in favor of Biden. The average precinct size is 1073 votes.

Biden had a nearly 2-1 official victory margin over Trump across the state. Here are detailed statistics for the statewide election, again, from Dr. Weber.

Now, let’s apply Benford’s Law to identify possible fraud. Here is the statewide Benford chart, displaying the frequency of precinct tallies’ leading digits for Biden and Trump, compared to Benford targets. The official data have separate tallies for votes cast In-Person and by Vote-by-Mail for each precinct. Given this, there are roughly twice the tallies than the number of active precincts in the state.

This chart looks almost normal. The Benford targets (i.e., the expected leading digit percentages) are shown by gray vertical bars. Trump’s precinct leading digit percentages are shown by the orange line; Biden’s are the blue line. If we look closely, however, the Biden line is low in leading digits for 2, 3 and 4, and high in leading digits for 1, 6, 7, 8, and 9.

The Chi-Squared test is a statistical tool that measures the correlation of two curves (two sets of sequential data) and provides a numerical value for the correlation, with zero percent indicating little or no correlation, and 100% indicating high correlation. The Chi-Squared test is calculated and provided to show the candidates’ precinct tally data’s closeness to the Benford target curve.

Now let’s isolate the data for sources of fraud. Here is the equivalent state-wide chart for In-Person votes only.

These In-Person precinct tallies’ leading digits for both Trump and Biden correlate very closely to the Benford targets. The average precinct in-person vote counts are 103 Biden, and 97 Trump. The 15,226 precinct tallies come in all sizes, and the tallies are distributed across the leading digits as Benford predicts. These In-Person votes represent 17.8% of all official votes tallied. There is little or no fraud indicated by Benford’s Law here.

The trend we saw on the first statewide Benford chart is more pronounced here, with Biden low in leading digits 2, 3, and 4, and high in 5, 6, 7, 8, and 9. The average official vote-by-mail count per precinct here is 600 Biden, 284 Trump. Notwithstanding these averages, the Biden precinct tallies are low in the 200s, 300s, and 400s, and high for the 500s, 600s, 700s, 800s, and 900s. The Trump tallies look high in the 200s through 500s and low in the 700s through 1,000s. Across 15,787 statewide precincts (a large sample size), this information, and the resulting “X-Pattern” of the candidates’ lines above, indicates likely manipulation of naturally generated precinct tallies.

I ask the reader, does this chart, for which the official statewide ballot total is nearly 2:1 in favor of Biden, look right to you? The blue, Biden, bars are much higher than 2:1 for the higher candidate precinct totals. What happened here? Did someone, somewhere, stuff the Vote-By-Mail ballot tallies with Biden votes generating the much higher Biden VBM totals?

Officially, Biden received 185% of the Trump votes (64.9/35.1%.) Here are the data for the chart above. (Pardon the detail, but this is key.) I ask the reader to compare the RH column of the table below with the 185% Biden/Trump ratio for the overall race and draw your own conclusion.

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