Odds Hillary Won Without Widespread Fraud: 1 in 77 Billion Say Berkeley, Stanford Studies


After applying various statistical models to subsets of 2016 primary voting data several academic researchers conclude Hillary Clinton’s win was only possible through widespread vote fraud.

WIDESPREAD ALLEGATIONS of election fraud and voter suppression across the United States during the 2016 Democratic Primary has sparked the interest of several academic researchers and what they discovered in their research is disturbing.

The researchers each performed independent studies in which a few different statistical analyses were applied to analyze various subsets of vote data and of the studies came to the same conclusion.

Namely that Hillary’s win could have only been possible a result of widespread election fraud.

In fact, one of the statistical models applied by Stanford University researcher Rodolfo Cortes Barragan to a subset of the data found that the probability of the “huge discrepancies” of which “nearly all are in favor of Hillary Clinton by a huge margin” was “statistically impossible” and that “the probability of this happening was is 1 in 77 billion”.

Furthermore, the researchers found that the election fraud only occurred in places where the voting machines were hackable and that did not keep an paper trail of the ballots.

In these locations Hillary won by massive margins.

On the other hand, in locations that were not hackable and did keep paper trails of the ballots Bernie Sanders beat Hillary Clinton.

Analysis also showed repeatedly irregularities and statistically impossible reverses in reported live votes in several locations across the country.

In commenting on the research, Barragan stated that some of the models are rock solid and 59 years old and the results seen here have never been witnessed in non-fraudulent election during that time period.

To summarize, at least four different independent studies were conducted  with various statistical models applied.

The researchers applied the different statistical models to:

  1. Actual vote counts as they were reported
  2. Discrepancies in polling data versus actual counts.
  3. Various subsets of demographic polling data versus actual vote counts

The results of each study corroborated with the results of the others and some of the researchers have reviewed the work of the others and gone on to confirm the findings in those studies.

It will take months for the studies to undergo peer review.

However, all of their research statistically proved there must of been widespread fraud to create the discrepancies in the vote counts that exist in all 3 subsets of the data analyzed.

The research of done collaboratively with Axel Geijsel of Tilburg University in The Netherlands.

That research corroborates independent mathematical research conducted by Richard Charnin.

Further independent research was conducted by Beth Clarkson of Berkeley who also not only corroborated the two previous studies but reviewed them and after her research was done and confirmed their results.

A PDF Summary of the Barragan/Geijsel study “Are we witnessing a dishonest election? A between state comparison based on the used voting procedures of the 2016 Democratic Party Primary for the Presidency of the United States of America” can be found here.

The meat of the study is contained in the Appendix, Supplemental Analyses, and References to Barragan’s Study and in the attachments which follow.


This report summarizes the results of our review of the GEMS election management system, which counts approximately 25 percent of all votes in the United States. The results of this study demonstrate that a fractional vote feature is embedded in each GEMS application which can be used to invisibly, yet radically, alter election outcomes by pre-setting desired vote percentages to redistribute votes. This tampering is not visible to election observers, even if they are standing in the room and watching the computer. Use of the decimalized vote feature is unlikely to be detected by auditing or canvass procedures, and can be applied across large jurisdictions in less than 60 seconds.

They allow “weighting” of races. Weighting a race removes the principle of “one person-one vote” to allow some votes to be counted as less than one or more than one. Regardless of what the real votes are, candidates can receive a set percentage of votes. Results can be controlled. For example, Candidate A can be assigned 44% of the votes, Candidate B 51%, and Candidate C the rest.

Instead of “1” the vote is allowed to be 1/2, or 1+7/8, or any other value that is not a whole number.

Fractions in results reports are not visible.Votes containing decimals are reported as whole numbers unless specifically instructed to reveal decimals (which is not the default setting). All evidence that fractional values ever existed can be removed instantly even from the underlying database using a setting in the GEMS data tables, in which case even instructing GEMS to show the decimals will fail to reveal they were used.


Even when adjusting for the proportion of black voters in a state, the amount that votes for Clinton is still disproportionally higher.

[note from the writer, this might indicate that if tampering with the votes has occurred, it would be reasonable to assume that they are added to subgroups which are claimed to heavily favor Hillary Clinton, i.e. black and female voters (for the latter I have not found the time yet)]


In the above polls done by Gallup and Pew research center Sanders scores a higher favorability ratings than Clinton. In all the ratings, conducted by these renowned institutes, they found that the favorability ratings for Sanders consistently outperformed Hillary Clinton, with mixed results in the subgroup of African American voters. The last being one of the biggest claimed subgroups which would favor Hillary Clinton. This is in stark contrast with the results in the non paper-trail states, where Clinton won the African American vote with 83%. In the paper-trail states, she only won them with 74% of the votes. The latter lying far closer to the polling results.

Not just that, Sanders outperforms Clinton in almost all the groups and subgroups in these polls, which is in stark contrast with the end results from the primaries. These results in earlier elections often lied very closely to the actual final results.

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Pew_2In the following pages, graphs are shown containing the cumulative placed votes over time. In sampling, polling, or any other form of statistical analysis. The general rule is that the higher the amount of trials that one does, the more you would get closer to the actual ‘true’ number. Meaning, the more votes that are placed, the more chance that the number that is given is correct.

Because of this, at the start of the polling, the numbers might fluctuate heavily, after which they will stabilize over time. Similar to an 1/x graph. On the following three pages, you will find numerous examples in which the graphs will indeed smoothe out. These are examples of graphs as you would normally find them.

On the three pages thereafter, you will find abnormal curves. Incidentally, all of these changes favored Hillary Clinton. Below the graphs, you will find the p-value as we found through our own proportional analysis. Meaning, the smaller the p-value, the higher the discrepancy between the exit-polls and the final results (i.e. indicating the chance of such an occurrence; e.g. p=0,07 is a 7% chance). These are indications of election fraud taking place.

Most of the normal curves are retrieved from the New York Times website. The abnormal curves have been retrieved from the website of — .

The reason for this is because the abnormal graphs have been removed from the mainstream media websites.

“One can also search for trends to check for fraud. One of the most revealing methods, the Cumulative Vote Share Analysis, searches for a correlation between the size of a discrepancy (between recorded vote and exit polls) and the size of a precinct. When no fraud has taken place the trend tends to be quite regular. When the discrepancy tends to manifest as the size of the precinct becomes larger than a certain value, it is a strong indication of fraud, according to Richard Charnin. Roughly speaking the reason for this behavior is that electronic rigging is implemented strategically in order not to become obvious. The discrepancy caused by the rigging is “better” distributed between those precincts that are big enough to be worth the effort.”


Looking at the discrepancies between the exit polls and the final tally, nearly all are in favor of Hillary Clinton by a huge margin. This is statistically impossible (“The probability P of this happening is 1 in 77 billion”).

“A discrepancy between the declared vote (recorded vote) and the vote extrapolated from the exit polls is an indication of fraud when it is above a margin of error of 2% within a confidence level of 95%.

Here is how it works. When statisticians try to measure the ‘real vote’ they not only estimate the final vote count but they also analyze the entire distribution of the data they gathered from the exit poll voter sampling in order to determine the reliability of their final determination. When fluctuations in the data are due to randomness they will follow a statistical distribution that follows the shape of a bell curve, the Gaussian curve. The reliability or unreliability of the sample data doesn’t depend so much on the trustworthiness of those who collect the exit poll voter sampling, but it’s rather intrinsic to the shape of the distribution. From this shape an ‘interval of confidence’ is determined within which we can unquestionably claim our confidence that we got it right with a probability of 95%–always 95%. This interval of confidence is also called ‘margin of error’ (MoE).

Poorly informed ‘experts’ frequently argue that the statistical analysis of exit polls can be misleading because it assumes that real life data is randomly distributed (as in the Gaussian curve) when that’s not always the case. And here is where they are missing a central point. The expectation that sample data will be randomly distributed ALREADY takes into account all possible relevant factors in a practical observation in real life. When extraneous factors intervene, a discrepancy will make the recorded value fall outside of the interval of confidence signaling only one possibility: a systematic error. When this occurs statisticians make further analysis to determine the causes, and either remove the cause or include it into the ‘margin of error’. After 59 years of fine-tuning this process in countless elections around the world statisticians have reached a point where exit polls have become extremely reliable. If the final ‘Recorded Vote’ falls outside the interval of confidence one can assume with a high degree of certainty that the systematic error is intentional. This is why we say that we have a high probability of fraud.”

“My specialty is statistics and I’ve pulled down publicly available data independently, analyzed it myself, and corroborated analyses which points to massive widespread election fraud. Mr. Holland disparages the mathematical work of Richard Charnin*, but I have not found an error in any of the analyses of his that I have repeated.

In particular, his assessment of the binomial probability regarding the likelihood of the exit poll results, is both accurate and appropriate. I have verified it myself. This binomial analysis was ignored by Mr. Holland in favor of criticizing a different approach that was also used. That approach is also sound, but I have not reproduced those calculations. That both models show results that are consistent with the hypothesis of election fraud is more than doubly damning.

If we assume no election fraud, then the two different types of analysis of the exit poll errors are unrelated because one analysis looks at the size of the error while the other is based on whether it benefited Hillary versus Bernie. That they are both consistent with fraud could be considered a third piece of evidence in support of that hypothesis.
There are only two possibilities – a) Bernie supporters are more likely to respond to the poll or b) there is widespread election fraud altering election results in favor of Hillary across the U.S.

Cumulative Vote Share (CVS) analysis pioneered by Francis Choquette shows problems across the nation for the past decade or more. Interestingly enough, places that use hand counted ballots do not show the same trends and within a state, analyzing by machine can show sharply different trends for different equipment. Such analysis shows trends that are indicative of rigging that favors Hillary.

The apparent ease of hacking electronic voting machines combined with the prevalence of election rigging through-out the world and human history.
Lack of basic quality control procedures: In most locations in the U.S., no one – not officials and not citizens – actually verify the official vote counts. Canvassing becomes a sham that involves verifying that yes, the machine produced outcomes all add up to the machine produced totals. In those places where the count was supposed to be publicly verified,citizens watching report blatant miscounting to force a match to the “official results”. Their testimony to election commissioners about such actions were met with a blank stare followed by dismissal of their testimony.

I do not make that statement lightly. I hold a Ph.D. in statistics and have been certified as a Quality Engineer for nearly 30 years. I’ve gone to the extreme of filing a lawsuit requesting access to the voting machine records to verify those election results. So far, I haven’t been allowed access.

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Source: Higgins News Network

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