Tag Archives: exit polls adjusted to match the recorded vote

2016 Election: illegal voters, uncounted votes, machine vote flipping

Richard Charnin
Updated Sept. 19, 2017

77 Billion to One: 2016 Election Fraud
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts
Proving Election Fraud: Phantom Voters, Uncounted Votes and the National Poll

Clinton won the recorded vote by 2.8 million. But the recorded vote is never equal to the True Vote due to election fraud.

There is evidence that millions of illegals probably voted in 2016. View this 1988-2016 trend analysis of Hispanic voter registration and turnout.

According to Greg Palast, least one million Democratic minority voters were disenfranchised via Crosscheckwhich eliminated voters with duplicate names from voter rolls. He claims that 7 million minority voters were disenfranchised.

There is evidence that  George Soros , a Clinton backer,  controls voting machines in 16 states.  Election analyst Bev Harris has posted Fraction Magic , an algorithm used to flip votes on Central tabulators.

Sensitivity analysis shows the effects of a range of assumptions on the vote count.

Let TV = True Vote; RV = Recorded vote
RV = TV + Fraud

Given the Recorded vote in millions:
Clinton 65.7, Trump 62.9, Other 7.6

Election fraud components:
-Vote flipping on maliciously coded, proprietary voting machines and central tabulators
-Illegal voters (non-citizens)
-Uncounted votes (spoiled ballots, disenfranchised voters)

Base Case Assumptions
Uncounted- 7 million: 85% for Clinton
Vote Flip- 5 million (net): 8% of Trump’s votes flipped to Clinton on voting machines and central tabulators. 
Illegals- 2 million: 85% for Clinton
Trump wins by 3.7 million: 68.7-64.9 (48.6-46.0%)

Assume 12 million uncounted: 85% to Clinton 
(2 million illegal, 5 million flip)
Trump still wins: 69.4-69.2 million (47.48-47.32%)


Illegal… 2.0…….  -1.70…..  -0.30…………0 non-citizens
Unctd…..7.0………5.95……..1.05…………0 disenfranchised 
Flip……..5.0…….  -5.0……….5.0………….0 voting machine


Sensitivity Analysis (assume 7 million uncounted, 85% for Clinton)
Worst case (7% flip, 80% of illegals to Clinton):  Trump wins by 2.3 million
Base case: (8%  flip, 85% of illegals to Clinton): Trump wins by 3.7 million
Best case: (9% flip, 90% of illegals to Clinton): Trump wins by 5.2 million

View the spreadsheet:

Total Clinton Trump Other
Recorded vote 136.2 million 65.7 62.9 7.6
48.25% 46.17% 5.59%
Illegal 2.0 -1.7 -0.3 0
Uncounted 7.0 5.95 1.05 0
Vote Flip 5.0 -5.0 5.0 0
Adjusted 141.22 64.9 68.7 7.6
  46.0% 48.6% 5.4%
7.0 million uncounted 85% to Clinton
Illegals to 
  80% 85% 90%
Flip to Clinton   Trump Vote
9% 69.20 69.30 69.40
8% 68.57 68.67 68.77
7% 67.94 68.04 68.14
Vote Flip   Trump Vote
9% 49.00% 49.07% 49.14%
8% 48.56% 48.63% 48.70%
7% 48.11% 48.18% 48.25%
Vote Flip   Clinton vote
9% 45.61% 45.54% 45.47%
8% 46.06% 45.98% 45.91%
7% 46.50% 46.43% 46.36%
Vote Flip   Trump margin
9% 4.79 4.99 5.19
8% 3.53 3.73 3.93
7% 2.27 2.47 2.67
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Posted by on September 20, 2017 in 2016 election


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Cumulative Vote Share Anomalies: Indicators of Rigged Elections

Cumulative Vote Shares: Summary and Index

Richard Charnin
August 17, 2015
Updated:  Dec.11, 2015

Look inside the books: Reclaiming Science: The JFK Conspiracy
Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts

Compendium of Links to all of my posts

2004 Election Fraud: Confirmation of a Kerry Landslide
1988-2012 Presidential Elections: The Master Spreadsheet
Cumulative Vote Share Spreadsheet Reference

Questions have been raised as to whether the number of elections analyzed is sufficient to draw conclusions. Given that approximately 20 million votes in 13 elections have been analyzed, the results are statistically significant. The analysis is confirmed by other forensic methods (True Vote Model, exit polls) for competitive and non-competitive races.

The analysis of cumulative vote shares (CVS) has revealed a consistent pattern. It is a well-known fact that Democrats are the majority in highly populated urban locations; the largest precincts are usually Democratic. Republicans are heavily represented in rural areas. But in scores of state elections there has been an increase in cumulative Republican vote shares in larger precincts. This anomaly has been noted by PhDs in Kansas and Vanderbilt University.

The basic premise is that Republican increase in cumulative precinct vote shares is counter-intuitive since the Democrats do much better in urban and suburban counties than in rural areas where the GOP is dominant. Precincts in Urban areas contain more voters than rural areas.

Since the GOP gains share in Democratic locations in virtually all of the competitive elections analyzed, it is highly suggestive evidence that Democratic precincts are where the majority of votes are stolen. In competitive elections, the correlation between county/precinct vote-size and the change in Democratic vote share is negative; Democrats lose share as county/precinct size increase. On the other hand, in non-competitive races, the Statistical correlation is close to zero; there is virtually no relationship.

The numerical evidence in each election is clear.
1- In the 15 largest counties, Republican vote share increases from the 25% mark to the final.
2- In the other smaller counties, there is virtually no change in vote share from the 25% mark.
3- In counties where the Democrats led at the 25% mark, their vote share declined significantly.

This post links to CVS blog posts and related spreadsheets. View the CVS Summary graph.

On Nov. 5, 2015 I posted this CVS analysis for the KY Governor race. We see the same counter-intuitive vote shares in the largest counties.

Consider the following changes from the 25% cumulative vote share to the final recorded share  in six Governor elections: Table of CVS changes. Compare the CVS anomalies to the non-competitive races:

SD Gov: Daugaard (R) won  by 70.5-29.4%. Note the slight 0.49% change in vote shares.
KS senate: the Independent lost by 53.2-42.2% but nearly tied the Republican in the Top 15 counties (618,000 votes). In the other counties (264,000 votes), he lost by 64.3-31.3%.

In the Maryland  election, Hogan (R) defeated Brown (D) by 65,000 votes (51.7-47.2%). But Brown won the 301,000 early and 83,000 late votes (absentee and provisional paper ballots) by 53.9-44.5%. Hogan led on Election Day  voting machines (1,319,000 votes) by 52.9-45.3%.

This anomaly also occurred in the 2000-2012 presidential elections. The Democrats did much better in early and late voting. In 2012 Obama led in early voting (40 million) by 55-43%; he led in late voting (11.7 million) by 58-38%. He lost to Romney on Election Day (77 million) by 50.4-47.9%.

Additional proof of Governor election fraud is that Exit polls (check Party_ID) are always adjusted to match the recorded vote.

Other studies
– A Vanderbilt Univ. statistical study of precinct level data in US presidential elections reveals a correlation of large precincts and increased fraction of Republican votes.

– Wichita State University engineering professor and statistician Beth Clarkson has accused three states — Wisconsin, Ohio, & Kansas — of voting irregularities that indicate a tampering of electronic voting machines.

Cumulative Vote Share (CVS) anomalies were noticed in a 2005 Ohio special election election favoring Schmidt (R) and in the 2012 GOP primaries favoring Romney.

Michael Collins wrote this article on the 2005 race: “Richard Charnin, posting as TruthIsAll, first noted the pattern with an analysis of the 2005 special election for a vacated seat for Ohio’s 2nd district, in the House of Representatives. The candidates were the liberal-populist Democrat Paul Hackett versus a right-wing Republican, Jean Schmidt. Charnin noticed that Schmidt’s votes and percentages increased substantially from the smallest to largest precincts in that district. This was a patently absurd pattern of vote accumulation since the liberal Hackett wins were in highly conservative counties that rarely voted for any Democrat.

Precincts with the most votes favored Schmidt at nearly 100%, with Hackett winning in only those with less than 200 votes counted. A review of precinct level results by Charnin on Democratic Underground reveals this interesting trend. This data is preliminary and more detail needs to be obtained from the Clermont Board of Elections. However,the data observed for Clermont makes little sense on the face of it.

Hackett won 38 of 191 Clermont precincts with fewer than 187 votes, but lost ALL of the largest 54 precincts (those with more than 187 votes each). This is reflected in a graph produced by Democratic Underground poster TruthIsAll, one of the first election fraud analysts to notice anomalies in Clermont County. Hackett’s percentage by precinct group size:
46.9% in precincts under 100 votes
43.5% in precincts of 100-200 votes
39.6% in precincts of 200-300 votes
34.6% in precincts of 300 + votes

These results raise interesting questions. Why does Hackett do much better in the smaller precincts? Are they more rural than the larger precincts? If so, does this not present a counter-intuitive pattern, with the Democrat taking some of the conservative, less populated areas and the Republican winning all of the precincts in the most populated areas?

A question can be raised about the difference between turnout (the votes cast) and the actual size of the precinct, which may or may not be a reflection of votes cast. The following graph, also produced by Charnin, answers the question.  

As he said while commenting on this data on 8/5/05: “The regression line has zero slope. Voters turned out at a fairly constant rate across precincts. So turnout wasn’t a factor in explaining why the Schmidt vote percentage increased as precinct size increased.”

Collins also wrote a two-part article on the 2012 GOP primaries in which  a CVS analysis showed a consistent pattern of votes being flipped to Romney. “Part I of this series suggested that there may well have been massive vote flipping for candidate Mitt Romney in the Republican primaries (Rigged Elections for Romney (10/22/12) The article and the initial research analysis were received broadly. In addition, highly motivated citizens across the country and a team of high school students contacted the authors for help replicating the research in their states. The researchers, Francois Choquet et al., point out that this can be done with their open source techniques.

The basic argument is straightforward. If you look at precinct level voting data arranged from the smallest to the largest precincts, you will see Romney’s gains increasing substantially as the cumulative vote increases. For example, Ohio and Wisconsin show this clearly as do eleven other states presented here. This extraordinary vote gain from smallest to largest precincts is so out of line, that the probability that this would happen by chance alone is often less than 1 out of a number represented by 1 preceded by 100 zeros and a decimal point, a value beneath the statistical package’s lower limits. As a result, the researchers termed the suspected vote flipping for Romney the “amazing anomaly.” (The Amazing Statistical Anomaly)

The research team’s observation of Romney gains based on precinct size is not unique. The anomaly was raised previously concerning the Republican presidential primaries on a political discussion forum.”

This review of Jonathan Simon’s book Code Red mentions CVS analysis:

Simon discusses another kind of evidence pointing to suspicious election outcomes. “The evidence is based on a method called “cumulative vote share” (CVS) analysis in which a graph is made for county results that shows the cumulative vote percentage by adding in precincts according to the precinct size, with the smallest precincts included first. According to statistical theory, the resulting graph should look something like the following result for DuPage County in the 2014 Illinois Governor race. Note: these graphs were produced by Richard Charnin and presented at his web site. 

These curves are fairly flat on the right side as the larger precincts are included in the vote totals, which means that vote percentages did not change a great deal with precinct size. This is what is expected. However, some counties showed a result that is unexpected from a statistical viewpoint. Here is the same plot for Peoria County in the same election:

This kind of graph, in which the Republican’s result shows an increasing percentage, while the Democrat’s percentage is decreasing is repeated in many races in many states throughout the country. Again, these patterns are not proof of election fraud, but they raise questions and underscore the need for a transparent, auditable system that can be trusted by the voters. Why the public is not more alarmed about the distressing state of our election system is a question that perplexes and frustrates Simon. Perhaps his book, along with the work of other election integrity activists will eventually rouse the public to demand reform. He expresses these sentiments with eloquence in the book:

“I’d like to think this story will have a happy ending, that history will review in appreciative terms the struggle of a few activists—Cassandras really—to prod leaders and public alike to scale the towering Never-Happen-Here Wall Of Denial so that they can then act together to restore the essential element of observable vote counting to our nation. Most truths eventually come out. All we can do is keep trying in every way possible to help this one find its way into the light.” (CODERED, p. 14)

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Posted by on August 18, 2015 in 2014 Elections


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