Monthly Archives: April 2011

An Open Letter to Nate Silver from Richard Charnin

An Open Letter to Nate Silver from Richard Charnin

Richard Charnin (TruthIsAll)

Updated: Aug. 2, 2010
Updated: Aug. 25, 2012

Links to related posts:

An Open Letter to Nate Silver from Richard Charnin

Go here for the full post which contains many links for further analysis and numerical tables:

Nate, since your recent hiring by the NY Times, the R2K flap and your exchanges with Zogby, you have been getting lots of publicity from blogs such as vanity fair and Your characterization of Zogby’s expertise (that he is the “world’s worst pollster”) says more about you then it does about him. Zogby ranked #1 in 1996 and 2000 (yes, Gore won Florida, despite what the NY Times said), and came close in the 2004 and 2008 elections, yet you fail to give him credit and rank him dead last. Why? Because you go along with the media-perpetuated myth that the recorded vote is sacrosanct. In other words, you discount the fraud factor and fail to distinguish between the True Vote and the recorded vote.

Below, you will see why Gore won by perhaps three million more than his recorded 540,000 vote margin; why Kerry won the True Vote by 10 million; why the Democratic Tsunami was denied in the 2006 midterms; and why Obama won by nearly 22 million votes in 2008, not the 9.5 million recorded.

I hereby challenge you to try and debunk the data, logic and mathematics used in the True Vote Model. If you cannot do so, then the underlying premise of your ranking system (that the recorded vote is an appropriate baseline to measure pollster performance) is invalid.

As an Internet blogger who has been posting pre-election and exit poll analyses to prove election fraud since 2004, I have occasionally looked at your postings on I will say right here that unlike the bloggers and mainstream media (MSNBC, the NY Times, etc.) who extol your forecasting “expertise”, I do not believe you are the statistical wiz they claim you are.

I say this as one who has been building quantitative models since 1965 for defense/aerospace manufacturers, Wall Street investment banks and has consulted for many financial and corporate enterprises. I have three degrees in Mathematics, including an MS in Applied Mathematics and an MS in Operations Research.

Your 2008 simulation model win probabilities did not sync with the projected vote shares. The major flaw in your model was to conflate it with your pollster rankings, an ill-conceived methodology. The first rule of model building is KISS (keep it simple stupid). You not only introduced an extraneous variable into your model, but the rankings were incorrect – a double whammy. Now, what do I mean by this, you ask?

You fail to distinguish the True Vote from the Recorded vote by ignoring vote miscounts. The premise on which your models are based (that fraud does not exist) is incorrect from the get-go. In your ranking system, pollsters who come close to the recorded vote (i.e. Rasmussen in 2004) are ranked high, but pollsters who come close to the True Vote (i.e. Zogby) are ranked low. The fact that Zogby is ranked at the bottom is a clear indictment of your approach. Ranking pollsters based on their performance against the recorded vote is a waste of time. Fortunately for you, your fans are unaware of the distinction between the recorded vote and the True Vote. In fact, most are unaware of the extent in which their votes have been compromised by fraud. In your models, election fraud is never a factor.

This is the simple, yet fundamental equation that you seem to be blissfully unaware of:
Recorded Vote = True Vote + Fraud Factor

In every election since 1968, the recorded vote has deviated widely from the True Vote. In the eleven elections, the Republicans won the recorded vote by 49-45%; the Democrats won the True Vote by the reverse: 49-45%.

In the 1988-2008 presidential elections, the Democrats won the unadjusted exit polls by 52-42%. But the margin was cut to 48-46% in the recorded vote. Check it out for yourself:
The 1988-2008 Unadjusted Exit Poll Reference

If you ever mentioned that fact (I doubt that you will) you would surely tell your readers that it was due to lousy exit polls. Of course. Tell it to the National Election Pool and Edison Research.

I bet you will never mention this fact: 226 of 274 presidential state exit polls “red-shifted” in favor of the Republican. The probability is
P =Binomdist(56, 68, .5, false)^4
P = 3.7E-31

Or would you mention that the the margin of error was exceeded in 126 of the 274 exit polls, of which 123 red-shifted to the Republicans?
The probability P = Poisson(123,.025*274,false)
P = 5.4E-106
Would you rather see 106 zeros to the right of the decimal?

But I digress. Back to Zogby. In 2004, Zogby’s final polling in nine battleground states was within 0.5% of the unadjusted exit poll average (after allocating undecided voters). Kerry led in 8 states by 50.2-44.8%. The base case assumption was that he would capture 75% of the undecided (UVA) vote and win all 9 states by 53.7-45.9%. Assuming a conservative 55% UVA scenario, he would still win 8 states by 52.7-46.8%. Kerry officially won 4 of the 9 states by 50.1-49.4%. The margin of error was exceeded in 7 states, a 1 in 4.7 billion probability.
In 1996, Zogby was within 0.3% of the recorded vote.
He ranked # 1.

In 2000, Zogby was within 0.1% of the recorded vote.
He ranked #1
But there were 6 million uncounted votes.
Gore won by at least 3 million votes.
The election was stolen.

In 2004, Zogby was within 1.2% of the recorded vote.
His Election Day polling had Kerry by 50-47%.
Kerry’s True Vote was 53.2% – a 10 million margin.
The election was stolen.

In 2006, Zogby ranked #7.
The pre-election Generic Poll Trend Model forecast a 56.4% Democratic Landslide.
The unadjusted National Exit Poll had 56.4%.
The landslide was denied.

In 2008, Zogby was within 2.2% of the recorded vote.
He ranked # 4.
Obama had a 58% True Vote share and won by 22 million votes.
The landslide was denied.

So why is Zogby at the very bottom of your pollster rankings?

Since you rank pollsters based on how close their polls match the recorded vote, I assume that exit pollsters Edison-Mitofsky are ranked at the top, since their final state and national exit polls always seem to match the recorded vote. So why don’t they release the unadjusted exit polls as well? These may actually reflect the True Vote. As one who purports to be a Quant, you should be interested in the statistical rationale for matching the final exit polls to a rigged recorded vote.

The National Exit Pool is the media consortium that sponsors the exit polls. The NEP includes the Washington Post, ABC, CNN, AP, CBS and Fox News. That’s plenty of MSM polling power. It is the height of hypocrisy to call for data transparency from R2K but not call for the release of raw, unadjusted precinct exit poll data from the NEP that would prove election fraud. The raw data would uncover precincts with vote count/exit poll discrepancies that cannot be explained by random error.

What are your thoughts about the 2010 primaries in MA, AR, SC and AL? Does the fact that Coakley won the hand-counts in MA indicate something to you? Does the fact that 40 AR precincts that favored Halter were closed down right before the election indicate something? What about the unknown, non-campaigner Greene winning in SC by 59-41% but losing the absentees by 84-16%? The DINOS on the state election commission refused to consider the recommendations of computer scientists to investigate the voting machines that were obviously rigged. In AL on June 8, the attorney general issued an opinion that an automatic recount does not apply in a primary election. Knowing all this, will you factor fraud into your 2010 projections – along with estimated turnout and final polling shares?

Do you want further confirmation that Kerry won in a landslide? As an “expert” analyst, you should have taken a close look at the 2004 National Exit Poll. If you had, you would have seen that the Final NEP as always, was forced to match the recorded vote by increasing the 2004 percentage mix of returning 2000 voters from 41% at 12:22am (13047 respondents) to an impossible 43% in the Final (13660) at 1:00am. Bush’s vote shares were also inflated to implausible levels.

According to the Final NEP, 43% (52.6 million) of 2004 voters were returning Bush 2000 voters. But it was impossible: Bush only had 50.46 million recorded votes. Based on voter mortality tables, 2.5 million Bush 2000 voters died prior to the 2004 election. Therefore, at most 48 million living Bush 2000 voters could have returned to vote in 2004. Assuming 98% turned out, then 47 million voted. There is your proof that the Final National Exit Poll inflated the number of returning Bush voters by 5-6 million phantoms.

Simple mathematics applied to a FEASIBLE, PLAUSIBLE NUMBER OF RETURNING 2000 voters and NEW 2004 voters (based on TOTAL VOTES CAST) shows that Kerry won by 10 million. You are welcome to try and refute the True Vote Model.

Do you want to see a proof that Obama won by nearly 22 million votes and not by the recorded 9.5 million? As an “expert” analyst, you should have taken a close look at the 2008 National Exit Poll. If you had, you would have seen that the Final NEP, as is always the case, was forced to match the recorded vote by adjusting the number of returning 2004 voters to an impossible level. According to the NEP, 46% (60 million) of 2008 voters were returning Bush 2004 voters and 37% were returning Kerry voters. That means there were 12 million more returning Bush voters than Kerry voters – and that’s assuming the myth perpetuated by the mainstream media (who you are now going to work for) that Bush won by 3 million votes in 2004. Do you believe it? How could that be?

But it’s much worse than that. If Kerry won by 10 million votes as the True Vote Model indicates (you are welcome to try and refute it) then there were approximately 10 million more returning Kerry voters than Bush voters. Assuming the same NEP vote shares that were used to match the recorded vote, Obama wins by 22 million votes, not the 9.5 million recorded.

The 2008 NEP indicated that 4% (5 million) of the electorate consisted of returning third-party voters. That was clearly impossible; only 1.2 million third-party votes were recorded in 2004. In their zeal to match the recorded vote, the exit pollsters had to create millions of phantom Bush and third-party voters.

In the eleven presidential elections from 1968 to 2008, the Republicans won the popular vote by 49-45%, (6% went to third parties). But the Democrats won the True Vote by 49-45%.

It’s all in my book: Proving Election Fraud: Phantom Voters, Uncounted Votes, and the National Exit Poll.

As the first analyst to use Monte Carlo simulation in the 2004 Election Model (and the updated 2008 Election Model), I applied extensive exit poll analysis in developing a post-election True Vote Model. It proves that not only were the 2000 and 2004 elections stolen, it is likely that 1968 and 1988 were as well. There were at least 6 million uncounted votes in 1968, 11 million in 1988, 6 million in 2000 and 4 million in 2004 – and the clear majority were Democratic (minority) votes.

The Edison Mitofsky 2004 Evaluation Report provides the exit poll discrepancies (WPE) of 238 state presidential election exit polls from 1988-2004. Of the 66 that exceeded the 3% margin of error, 65 favored the Republican. Was it due to reluctant Bush responders and/or exuberant Democratic responders? No, it was the result of millions of uncounted votes (mostly Democratic) and millions of phantom Bush voters.

The Final 2004 Election Model Projection (Monte Carlo simulation) projected Kerry would win a 51.3% share and 337 electoral votes. This closely matched the unadjusted aggregate state exit polls (52%) and the 12:22am National Exit Poll (51.2%). The True Vote Model indicated that Kerry had a 53.2% share. Of course Bush won by a bogus 50.7-48.3% recorded vote margin. How did your projections pan out?

In the 2006 midterms, the pre-election Trend Model (based on 120 Generic polls) projected a 56.43% share for the Democrats. The unadjusted National Exit Poll indicated a nearly identical 56.37%. The Final National Exit Poll was forced to match the 52% recorded vote. Nate, which one do you believe was correct? You are surely aware of documented miscounts in quite a few congressional elections, virtually all favoring the GOP (see FL–13, FL-24, OH-1, etc.). How did your projections pan out?

The Final 2008 Election Model Projection (Monte Carlo simulation) exactly matched Obama’s 365 electoral votes and was within 0.2%(53.1%) of his 52.9% share. But it was wrong. Obama did much better than that.

The final state pre-election likely voter (LV) polls did not fully capture the late shift to Obama. Had they been registered voter (RV) polls, adjusted for undecided voters, Obama would have had a 57% share. He had 57% and 420 EV in the True Vote Model. As shown below, the final Gallup RV tracking poll gave Obama a 53-40% margin. After allocating undecided voters, he had 57% – matching the True Vote Model. How did your projections pan out?

As one versed in statistics, are you aware that the expected electoral vote is the simple summation:
EV = sum[State Win Probability (i) * EV (i)], where i=1,51 states.

Do you see why only state win probabilities, based on the latest polling adjusted for undecided voters, are necessary to calculate the expected EV?
Do you now see why a simulation or “meta-analysis” is unnecessary overkill for calculating the expected (“theoretical”) electoral vote?
Do you understand that the only reason for running a Monte Carlo electoral vote simulation is to determine an EV probability distribution?

The 2008 Election Model Monte Carlo simulation required only 5000 election trials for the mean EV (365.8) to converge to the theoretical expected value (365.3) illustrating the Law of Large Numbers. Do you see why an electoral vote simulation of more than 5000 election trials is overkill?

So what does it all mean?

It means that any and all polling analysis that fails to consider voter mortality, uncounted votes and a feasible voter turnout is doomed to produce the wrong result. The correct result is the True Vote based on total votes cast. The wrong result is the recorded vote that ignores uncounted votes but includes phantom voters.

It means that the recorded vote, the basis for your rankings, never reflects the True Vote!

It exposes your ranking system, which places John Zogby (the only pollster to predict the True Vote in the last three presidential elections) at the bottom of a list of scores of obscure pollsters, as being fatally flawed.

It means that your comments disparaging exit polls, along with your failure to do post-election True Vote analyses, indicate that you are in sync with a moribund mainstream media that perpetuates endemic Election Fraud by withholding raw exit poll data. They accept the recorded vote as Gospel – just as you do in your rankings. You will fit in very well at the NY Times.

When will you incorporate the True Vote into your analysis? Why do you ignore the fact that the mainstream media (i.e. the National Election Pool, which includes the NY Times) is responsible for the impossible adjustments (made by the exit pollsters they employ) to the final 2004, 2006, 2008 state and national exit polls? They had to match the polls to corrupted recorded vote counts, come hell or high water – and will surely do so again in 2010.

You have questioned the R2K Democratic share of the 18-29 age group exceeding the 30-44 group in 20 of 20 races.

Table 1 shows the probabilities for all the age groups.
There was a 33% probability that the Dems would do better in the 18-29 group than the 30-44 group in all 20 races given the average two-party shares. The comparable probabilities were 77% for 45-59 and nearly 100% for 60+.

You have questioned the apparent lack of volatility in the 2008 R2K tracking polls.

Table 2 displays R2K daily statistics.
The margin of error is 1.96 times the standard deviation (a measure of volatility) at the 95% confidence level.
The standard deviation of Obama’s daily poll shares was 1.83%. It was 1.59% for the 3-day moving average.

Table 3 is a comparison of Gallup vs. R2K.
Gallup was a registered voter (RV) poll. R2K was a likely voter (LV) poll.
The average shares and volatilities (standard deviation) closely match.
There was a strong 0.70 correlation between Obama’s Gallup and R2K shares.
There was a good 0.50 correlation between McCain’s Gallup and R2K shares.

Gallup Change Change R2K Change Change
Obama McCain Obama McCain Obama McCain Obama McCain
Avg 49.65 42.90 0.15 -0.15 50.29 42.21 0.06 -0.02
Stdev 2.02 1.74 0.94 0.89 1.59 1.86 0.70 0.73

Table 4 compares the R2K tracking poll and other polls (including standard, non-tracking polls)
Projections are based on the allocation of undecided voters (UVA).
1) 75% of the undecided vote is allocated to Obama, the de-facto challenger.
2) third parties have 1.5% (the actual recorded share).

The final Gallup projection (57.1%) for Obama is a close match to the True Vote Model (57.5%).
Obama projected shares:
Gallup: 53 + .75 * 5.5 = 53 + 4.13 = 57.1%
R2K: 51 + .75 * 3.5 = 51 + 2.63 = 53.6%

Table 5 is a 2008 Pollster True Vote Ranking Chart (15 polls)
Gallup (RV) ranks #1 with a 57.1% Obama projection (after UVA)
CBS (LV) and ABC/WP (RV) are tied at #2 with a 56.6% share

Zogby is ranked #4 with a 55.1% share.

Pollsters with a GOP bias brought up the rear.
Battleground (LV) is ranked #14 with a 52.4% share
Rasmussen is ranked #15 with a 52.1% share.

Table 6 is a comparison of final RV and LV polls
The average LV poll had Obama winning by 50.3-44.0 before allocating undecided voters (UVA) and 53.4-45.1 after UVA.
The average RV poll had Obama winning by 53.3-39.5 before UVA and 57.6-40.9 after UVA
Zogby’s LV poll had Obama winning by 54-43 before UVA and 55.1-43.4 after UVA

Consider the final ABC and Gallup RV Polls (total 5293 sample, 1.8% MoE).
Combined, they had Obama winning by 53.5-40.5 before UVA and 56.9-41.6 after UVA

You rank Zogby dead last, yet his LV poll numbers are right in the middle of the RV and LV groups. He is closer to ABC and Gallup than Rasmussen, Hotline and FOX. You have lowered Rasmussen’s ranking but you still rank him much higher than Zogby. Rasmussen has a strong GOP bias. Hotline, FOX and Battleground also lean to the GOP.

Do you have any evidence that Zogby’s polls are biased? Do you still feel justified in ranking Zogby last?

Table 7 displays the post-election True Vote Model.
It closely matches the RV projections and proves that the NEP returning voter mix is bogus.

The Final 2008 Monte Carlo-based Election Model projected a 53.1% Obama share.
The 5000 election simulation trials produced a 365.8 mean EV.

Obama had 365.3 expected electoral votes, matching his recorded 365 total.
The Election Model exactly matched the recorded EV and was within 0.2% of the popular vote.
But it was wrong.

The EM understated Obama’s True Vote by using final state and national LV polls.
The True Vote model indicates that he had 57-58% and close to 420 EV!

Do you still believe that Obama’s 52.9% recorded share reflects the True Vote?
Do you still think that Obama had just 365 electoral votes?

Do you see why Likely Voter polls understate the Democratic share when there is heavy new voter registration and turnout?
Do you see why biased GOP LV Tracking polls brought down the average Obama projected share?

Do you see why your pollster rankings are arbitrary? They are not justified statistically in a system of rampant election fraud.

The MSM won’t discuss election fraud, much less interview honest election activists and researchers. And the MSM does not hesitate to characterize anyone who questions the official count as a “conspiracy nut”.

Like in this conversation on the 2008 election.

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Posted by on April 9, 2011 in Rebuttals


Probability Analysis and Database of JFK Assassination Witness Deaths

Probability Analysis and Database of JFK Assassination Witness Deaths

Richard Charnin (TruthIsAll)
Updated: Oct.2, 2014

Click Reclaiming Science:The JFK Conspiracy to look inside the book.

JFK Blog Posts
JFK Calc Spreadsheet Database
Tables and Graphs

There has been much discussion and controversy regarding the number of unnatural JFK-related witness deaths that occurred following the assassination. The deaths were a combination of homicides, suicides, accidents and undetermined origin. This analysis of the probabilities of the deaths occurring over 1-15 year time intervals has been updated in Executive Action: JFK Witness Deaths and the London Times Actuary.

There were at least 18 unnatural deaths of JFK-related witnesses within one year of the assassination. In a given year, one unnatural death would be expected in a random group of 1400. The probability that at least 18 would die unnaturally in one year is 1 in 1000 trillion (see the mathematical proof below).

JFK Calc is a spreadsheet database of 126 JFK-related witnesses, of which 122 occurred from 1964-78. The spreadsheet contains the date of death, witness category and connection to the case as well as detailed probability calculations. Of the 126 witnesses, at least 80 met unnatural deaths (homicides, suicides, accidents, unknown); the others were suspicious heart attacks and illnesses, etc. JFK Calc has all the information required for a robust analysis: a) known witness universe, b) official cause of death, c) average unnatural mortality rates and d) the relevant time period (1964-1978).

Some have questioned the relevance of the unnatural and suspicious witness deaths related to the assassination, but 68 were called to testify in four investigations: 30 testified at the Warren Commission, 18 were sought by prosecutor Jim Garrison at the Clay Shaw trial, 9 by the Church Senate Intelligence Committee, 25 at the House Select Committee on Assassinations (14 were sought in sought in two investigations). These witnesses were indisputably relevant – as were others who were not sought to testify.

The probability analysis is straightforward; it is not a theoretical exercise. It is a mathematical proof of conspiracy based on historic mortality statistics, death rates. The probabilities are calculated using the Poisson formula. This is a challenge to those who claim that the deaths do not prove a conspiracy: To substantiate your claim, you must refute the witnesses cause of death, unnatural mortality rates and the Poisson formula.


An actuary engaged by the London Times calculated the probability that at least EIGHTEEN witnesses would die within three years of the JFK assassination as 1 in 100,000 TRILLION. The calculation is mentioned in the 1973 film Executive Action based on a book by the original JFK researcher and lawyer Mark Lane. The film starred Burt Lancaster, Robert Ryan and Will Geer.

The actuary’s probability is actually very conservative. At least 42 JFK-related witnesses died unnaturally in the three years following the assassination. Using the 0.000220 WEIGHTED JFK-witness mortality rate, the probability is E-53 (1/TRILLION^4).

The number of deaths spiked during the 1977-78 House Select Committee on Assassinations (HSCA) investigation of the JFK and MLK murders. The HSCA determined that both were conspiracies.

Warren Commission apologists have suggested that there were many more than 1,400 witnesses. The FBI claimed to have interviewed 25,000. But how many were material? The probability that 25 of 25,000 witnesses would be murdered in the three years following the assassination is 2E-11 or 1 in 40 billion.

To put these numbers in perspective, there are approximately 7E17 (700,000 trillion) grains of sand on the earth and 3E23 (300 billion trillion) stars in the universe.


Who’s Who in the JFK Assassination by Michael Benson, provides information on more than 1,400 JFK-related individuals (suspects, victims, witnesses, law enforcement officials and investigators) involved in the assassination.The book is based on years of research using a wealth of data sources and a detailed analysis of the Warren Commission’s twenty-six volumes. The JFK Calc spreadsheet includes 97 witnesses listed in Who’s Who in the JFK Assassination.

Hit List: An In-Depth Investigation into the Mysterious Deaths of Witnesses to the JFK Assassination by Richard Belzer and David Wayne is a comprehensive analysis of fifty witness deaths and cites the probability calculations presented here.

Crossfire by Jim Marrs lists 103 individuals related to the assassination who died mysteriously from 1963-1978. The latest version refers to my analysis.


Suppose that on Nov. 22, 1963, 1400 individuals were selected at random from the entire U.S. population. Further suppose that within one year, at least 18 would die unnaturally under mysterious circumstances. Based on unnatural death mortality rates, only 1 in a random group of 1400 would be expected to die unnaturally.

There are two possibilities. The 18 unnatural deaths were…
1) unrelated. It was just a 1 in 1000 trillion coincidence.
2) related. There was a common factor -a connection- between them.

We can confidently rule out 1). Then if the 18 unnatural deaths were related, what was the connection?

Once you have eliminated the impossible, whatever remains, however improbable, is the truth. – Arthur Conan Doyle


There were at least 18 unnatural deaths of JFK-related witnesses within one year of the assassination. In any given year, only one unnatural death would be expected in a random group of 1400. The probability that at least 18 would die unnaturally in any given year is 1 in 1000 trillion (see the mathematical proof below).

The 18 deaths could not have been a coincidence. There had to be a COMMON FACTOR. It could have been a) they were interviewed by the Warren Commission, b) scheduled to be interviewed, c) were in the commission witness index or d) related and not interviewed. If they were JFK-related, the deaths were not random. One must therefore conclude that the assassination was a conspiracy.

Lee Harvey Oswald, the alleged assassin, was shot by Jack Ruby in front of millions of television viewers on Nov. 24, 1963. He was conveniently disposed of before he could get a lawyer after claiming that he was “just a patsy”. The transcript of Oswald’s interrogation was destroyed.

In 1977, seven top FBI officials died suddenly in the six months from June to November. Two had testified to the Warren Commission; two were #3 FBI officials; two were forensic experts. William Sullivan, a #3 FBI official, died from an “accidental” gunshot while hunting, just before he was scheduled to testify at HSCA. James Cadigan, an FBI document expert, died from a fall in his home. The others died from heart attacks.


In a response to a letter from the 1977 House Select Committee on Assassinations, London Sunday Times Legal Manager Anthony Whitaker wrote: “Our piece about the odds against the deaths of the Kennedy witnesses was, I regret to say, based on a careless journalistic mistake and should not have been published. This was realized by The Sunday Times editorial staff after the first edition – the one which goes to the United States – had gone out, and later editions were amended. There was no question of our actuary having got his answer wrong: it was simply that we asked him the wrong question. He was asked ” what were the odds against 15 named people out of the population of the United States dying within a short period of time” to which he replied -correctly – that they were very high. However, if one asks what are the odds against 15 of those included in the Warren Commission Index dying within a given period, the answer is, of course, that they are much lower. Our mistake was to treat the reply to the former question as if it dealt with the latter – hence the fundamental error in our first edition report, for which we apologize”.

That settled the matter for the HSCA which did not bother to ask U.S. mathematicians to analyze the probabilities. One must ask: Why not?

Whitaker obfuscated a very simple mathematical problem: to determine the probabilities of unnatural JFK-related deaths over relevant time intervals: 1, 3, 14 years. He did so by leaving out the word unnatural.

The Times legal manager made two fundamental errors. The first was an incomplete and misleading statement of the problem. He implicitly assumed deaths of all types, natural and unnatural. He did not distinguish between the two categories. The probability calculations must be based on the expected number of unnatural (not total) deaths.

The second error was the omission of relevant numerical data: He did not provide unnatural death mortality statistics. He failed to show the probability calculations. Why not? Was it because it would prove that the actuary’s calculation was essentially correct?

If the London Times was interested in the truth, it would have confirmed these results:

1. Incorrect problem definition: Calculate the probability that 15 named JFK-witnesses would die in one year. Given the 1964 unnatural death rate (0.000825), the probability is 0.000825^15 that 15 named individuals would die unnaturally. The odds that 15 named individuals would die of any cause is of course much higher.

2. Correct definition: Calculate the probability that at least 15 material witnesses in a known group would die unnaturally in one year.

Given the 1964 UNNATURAL MORTALITY RATE (0.000825), the probability that at least 15 of 1400 RANDOM individuals would die unnaturally in 1964 is 1 in 445 BILLION (2.0E-12).

Given the 1964 JFK-WEIGHTED AVERAGE UNNATURAL MORTALITY RATE (0.000163), the probability that at least 15 of 1400 JFK-related individuals would die unnaturally in 1964 is 1 in 6 BILLION TRILLION (1.47E-22). In fact, there were at least 21 unnatural JFK-related deaths in the first year, so the probabilities are even lower. Of course, the odds that at least 15 would die of any cause is much higher: 1 in 2.


The probability calculations are based on the 0.000815 average annual unnatural mortality rate in 1964-78.

The probability P of at least n unnatural deaths in a group of N individuals, for time period T years, given unnatural mortality rate R, is P(n)= Poisson(n, E), where E=R*N*T is the expected number of unnatural deaths. As E increases, the probability increases. The probabilities of various unnatural deaths for a range of witnesses is displayed in a Probability Sensitivity Matrix.

The Poisson distribution is used to calculate the probability of a rare event when the probability of an event (P) is very small and the number of trials (N) is large, and therefore the expected number of events (P*N) is a moderate-sized quantity.

The probability of an unlikely event is calculated in Excel as P = POISSON (n, a, type) where n is the observed number of events; a is the expected number; type is a logical value that determines the form of the probability distribution, discrete (False) or cumulative (True).

In 1964, the annual unnatural death rate was 0.000825. Therefore, the expected number of unnatural deaths in 1964 in a random group of 1400 was 1.155 = 000825*1400. In other words, we would normally expect approximately ONE unnatural death in the group. But there were n=18 unnatural witness deaths within one year of the assassination.
N = 1400 = number of witnesses
n = 18 = number of unnatural deaths in 1964
a = 1.155 = 000825*1400 = expected number of unnatural deaths

The Poisson formula is: P(n) = a^n * exp (-a) / n!
P (18) = 6.55E-16 = 1.155^18 * exp (-1.155) / 18!

The probability of EXACTLY n=18 unnatural deaths:
P (n) = Poisson (n, a, false)
P (18) = Poisson (18, 1.155, false) = 6.55E-16.
P (18) = 1 in 1,526,824,370,949,295

The probability of AT LEAST 18 unnatural deaths:
P (>17) = 1 – Poisson (17, 1.155, true)
P (>17) = 7.77E-16 or 1 in 1,286,742,750,677,285


At least 30 of 552 witnesses died suspiciously (n=18 unnaturally) in the T=15 years from 1964-1978. Normally, 7 unnatural deaths would be expected. Using the 0.000257 weighted witness mortality rate, the probability of at least 18 deaths is ZERO:
P = E-11 = 1- POISSON (17, 2.13, false)
P = 1 in 60 billion

Now let’s calculate the probability of at least 11 homicides. Using the annual 1964-78 national homicide rate (0.000084), we would expect one homicide among the 552 Warren Commission witnesses. E = 0.65 = 552*15*0.000084
The probability of at least 11 homicides is:
P = 1 – Poisson (n-1, E, true)
P = 1 – Poisson (10, 0.70, true)
P = 2.54E-10 = 1 in 4 billion

Four Investigations
Approximately 1100 material witnesses were sought to testify in four JFK-related investigations: Warren Commission, Garrison/Shaw Trial, Senate Intelligence Hearings and the House Select Committee on Assassinations (HSCA). At least 67 of the 1100 died under suspicious circumstances and are included in the JFK Calc witness database. It is obvious that the 31 WC witnesses who died unnaturally and suspiciously were relevant; they testified. The vast majority of the others sought in the three investigations that followed died suspiciously shortly before they could testify.

Of the 67 deaths, 39 were unnatural (28 homicides). Twelve died from suspiciously timed heart attacks and 12 from illnesses and natural causes. David Ferrie supposedly committed suicide a few days before he was scheduled to testify before a grand jury at the Clay Shaw trial in 1967. Sam Giancana was murdered before he had a chance to testify at the Church Senate hearings in 1975. George de Morenschildt supposedly shot himself the day he was notified of his interview by the HSCA. Seven (7) top FBI officials died within a six month period in 1977 prior to their scheduled testimony at the HSCA. And there were many more.

Unweighted and Weighted Mortality Rates (1964-78)
Nationally, accidents comprised 66% of unnatural deaths compared to 11% for homicides, but 34 of 78 (44%) JFK-related deaths were ruled homicides. The rates must be weighted by cause of death to calculate probabilities. If restricted to homicides, the calculation is simple as it is based on just one rate.

The weighted rate is the sum-product of the individual unnatural rates and corresponding deaths (34 homicides, 24 accidents, 16 suicides, 4 unknown):
R = 0.000246 = (34*0.0000808 + 24*0.000594 + 16*0.000130 +4*0.00001)/78

Age-adjusted mortality rates

– The age-adjusted rate is an index measure, the magnitude of which has no intrinsic value. It should be used for comparison purposes only.

– If it is appropriate to use age-adjustment, then the comparison should not be affected by the selection of a standard population. Conversely, if the comparison can be affected by the choice of a standard population, then it is not appropriate to age-adjust for that comparison.

– The standard population should not be ‘‘abnormal’’ or ‘‘unnatural’’ when compared to populations under study. Considering the amount of published material, there are advantages to using the U.S. standard population.

– Standardization is not a substitute for the examination of age-specific rates. While standardization is most often applied to a series of age-specific death rates, direct or indirect standardization can also be applied to variables other than age. For example, infant mortality rates can be adjusted for birthweight distribution.

“Most population-based mortality objectives and sub-objectives in Healthy People 2000 are tracked using age-adjusted rates from the National Vital Statistics System (appendix table I). The exceptions are deaths from alcohol-related motor vehicle crashes, all motor vehicle crashes, and work-related injuries (objectives 4.1, 9.3, and 10.1), which are monitored with crude death rates from other data systems. In addition, objectives that refer to specific age groups are tracked with age-specific rather than age-adjusted rates.

Although the age-adjusted death rate (ADR) is one of the most frequently used indexes of mortality, there is often confusion concerning the basic concepts of its construction, use, and interpretation. Some of the persistent issues include the appropriateness of the ADR as a summary measure, the validity of comparisons between ADRs, the method of calculation, and the appropriateness of alternate summary measures.”

1964-78 Average Unnatural Mortality Rates
Homicide (34): 0.000084
Accident (24): 0.000594
Suicide (16): 0.000130
Unknown (4): 0.000014
Total Unnatural (78): 0.000822

1964-78 Average Natural Mortality Rates
Cardiac: 0.004913
Cancer: 0.001991
Stroke: 0.001562
Other : 0.001025
Total Natural (44): 0.009385 /strong>

1964-78 Average Mortality
Natural.. 0.009385
Unnatural 0.000822
Total…. 0.010207

At least 18 deaths (13 unnatural); assume 454 witnesses

Normally, 2 unnatural deaths would be expected.
Using the 0.000209 weighted rate, the probability is
P = E-17 = POISSON (13, .29, true)
P = 1 in 100,000 trillion

WARREN COMMISSION- 418 witnesses (CIA)
Normally, 5 unnatural deaths would be expected.
Using the 0.00057 WC witness rate, the probability of at least 18 deaths is ZERO:
P = E-13 = 1- POISSON (17, 1.61, false)
P = 1 in 5 trillion

Probability of at least 11 homicides is ZERO:
P = E-11 = 1-POISSON(10, 0.53, false)
P = 1 in 70 billion

1400 MATERIAL WITNESSES (Who’s Who in the JFK Assassination)
1964-66: at least 42 officially ruled unnatural deaths

Normally, 3 would be expected.
Using the 0.000213 weighted rate, the probability is ZERO:
P = 2.75E-54 = POISSON (42, 0.89, false)
P = 1 in 1 trillion trillion trillion trillion

1964-78: at least 78 officially ruled unnatural deaths
Normally, 17 would be expected.
Using the 0.000247 weighted rate, the probability is ZERO:
P = 2.76E-62 = POISSON (78, 5.18, false)
P = 1 in 1 trillion trillion trillion trillion trillion

1964-78: at least 34 officially ruled homicides
Normally, 2 would be expected.
Using the 0.000084 average national homicide rate, the probability is ZERO:
P = E-31 = POISSON (34, 1.57, false)
P = 1 in 6 million trillion trillion

Probability that EXACTLY n out of 1400 witnesses die unnaturally in one year
(declines EXPONENTIALLY as n increases)

0 3.15E-01 3
1 3.64E-01 3
2 2.10E-01 5
3 8.09E-02 12
4 2.34E-02 43
5 5.40E-03 185
6 1.04E-03 963
7 1.71E-04 5,834
8 2.47E-05 40,409
9 3.18E-06 314,873
10 3.67E-07 2,726,172
11 3.85E-08 25,963,547
12 3.71E-09 269,751,135
13 3.29E-10 3,036,159,956
14 2.72E-11 36,801,938,859
15 2.09E-12 477,947,257,903
16 1.51E-13 6,620,914,395,190
17 1.03E-14 97,450,688,067,732
18 6.58E-16 1,526,824,370,949,295
19 4.00E-17 24,983,141,484,728,104
20 2.31E-18 432,608,510,558,062,460
21 1.27E-19 7,865,609,282,873,863,200
22 6.67E-21 149,821,129,197,597,360,000

Mark Lane is the lawyer who would have represented Oswald. He is the author of several landmark books on the Kennedy assassination. Rush to Judgment was the seminal book which debunked the Warren Commission.

In this video, Lane interviews Penn Jones, a JFK researcher who investigated the strange deaths of many assassination witnesses.
JFK-related witnesses and cause of death.
A summary of JFK deaths.
The Conspiracy Zone shows the analysis presented here.
The Men Who Killed Kennedy
The Forgotten Victims to a Genuine Conspiracy – Part 1
The Forgotten Victims to a Genuine Conspiracy – Part 2

Injury and Death Statistics


Posted by on April 8, 2011 in JFK


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