You have brought up two very well-known and often-discussed topics in conspiratorial circles. It is important to approach them with critical thinking, distinguishing between confirmed facts, official versions, and conspiracy theories.

1. Boeing, the company: cases related to employees, lawsuits, witnesses, etc.

In recent years, the Boeing company has faced serious problems related to manufacturing quality and safety, which have led to several catastrophes and incidents. In this context, high-profile cases have emerged involving "whistleblowers" – former and current employees who reported problems within the company.

Key cases and their context:

   
• John Barnett:        
             
  • Who he was: A former quality manager at Boeing who had worked for the company for 32 years.
             
  • What he alleged: Since 2010, he had been working at the North Charleston plant, where 787 Dreamliner aircraft are manufactured. Barnett claimed that Boeing was rushing production, which led to a reduction in quality standards. In particular, he stated that workers were installing substandard parts and that the oxygen systems on 787 Dreamliner planes might be defective.
             
  • Death: John Barnett was found dead on March 9, 2024, in his truck in a hotel parking lot in Charleston, South Carolina. Police stated that the cause of death was a "self-inflicted gunshot wound" (suicide).
             
  • Context of death: His death occurred while he was giving a deposition as part of a lawsuit in which he was a plaintiff against Boeing, alleging that the company had retaliated against him for his revelations. He was scheduled to testify on the third and final day.
             
  • Reaction: Barnett's death caused a huge stir and gave rise to much speculation about a possible "elimination," given his status as a "whistleblower" and the timing of his death. Barnett's family filed a wrongful death lawsuit against Boeing, alleging that the company drove him to suicide through harassment.
         
     
   
• Joshua Dean:        
             
  • Who he was: A former quality auditor at Spirit AeroSystems, a key supplier to Boeing (which manufactures fuselages for the 737 Max).
             
  • What he alleged: Dean had stated there were "egregious manufacturing defects" at Spirit AeroSystems, including improperly drilled holes in the 737 Max fuselages. He was laid off in 2023.
             
  • Death: Joshua Dean died on May 1, 2024, at the age of 45 after a rapidly progressive and aggressive bacterial infection (MRSA) that led to severe pneumonia and organ failure.
             
  • Context of death: His death occurred just two months after John Barnett's death, which heightened suspicion.
             
  • Reaction: Although the cause of death was medical, the speed of its progression and Dean's status as a "whistleblower" also raised serious questions and speculation.
         
     

Why might this be beneficial to the company and the "government/CIA"?

   
• For the company (Boeing):        
             
  • Elimination of witnesses: The main benefit is the potential elimination of key witnesses in lawsuits and investigations. The testimonies of people like Barnett and Dean could have cost Boeing billions of dollars in lawsuits, fines, and reputational damage.
             
  • Intimidation of others: Cases like these can serve as a powerful signal to other potential "whistleblowers," demonstrating the possible risks and consequences. This could suppress the desire to speak the truth.
             
  • Information control: "Suppressing" information about defects and violations allows the company to avoid immediate and costly corrective actions, preserve its reputation for some time, and avoid intervention from regulatory bodies.
         
     
   
• For the government/CIA (in a general context):        
             
  • National interests: Boeing is one of the largest defense contractors in the U.S. and a key player in the aerospace industry. Problems with Boeing could be viewed as a threat to national security, economic power, and the ability of the U.S. to compete on the global market.
             
  • Suppression of criticism: The "government" (in the form of certain elements, not necessarily the CIA directly) may be interested in suppressing information that could harm a major corporation considered to be strategically important.
             
  • Connection to the CIA: A direct connection between the CIA and these specific cases has not been confirmed by any official investigations. Nevertheless, in conspiracy theories, the CIA often appears as a universal tool of the "deep state" for "eliminating" undesirables. If there were a threat of exposure that could seriously undermine the defense capabilities or economic security of the U.S., it could theoretically become a matter of interest for intelligence agencies. However, this remains purely speculative.
         
     

It is important to emphasize that the official versions of the deaths of Barnett (suicide) and Dean (bacterial infection) do not indicate a criminal nature. Nevertheless, suspicion and questions remain due to the unusual confluence of circumstances.

2. The Clinton family and the strange deaths associated with them

The conspiracy theory about the "Clinton body count" has existed for decades and claims that Bill and Hillary Clinton (or their associates) are responsible for the deaths of dozens of people who were their political opponents, witnesses, or had compromising information.

Examples of frequently mentioned deaths (without asserting their truthfulness):

   
• Vince Foster: Deputy White House Counsel under Bill Clinton. His death in 1993 was officially ruled a suicide. However, numerous conspiracy theories claim that he was murdered because he had information related to various Clinton scandals (e.g., Whitewater). Multiple investigations (by the FBI, independent prosecutors) have confirmed the suicide theory.
   
• James McDougal: A Clinton partner in the Whitewater Development Corporation, a key witness in the "Whitewater" scandal investigation. He died of a heart attack in prison in 1998. Conspiracy theories claim his death was suspicious because he had begun cooperating with prosecutors.
   
• Mary Mahoney: A former White House intern, killed in 1997 during a robbery at a Starbucks. Conspiracy theorists claim she was about to reveal information about sexual harassment at the White House.
   
• John F. Kennedy Jr.: The son of President John F. Kennedy, who died in a plane crash in 1999. Conspiracy theories claim his death was "convenient" for Hillary Clinton, who was preparing to run in the New York Senate election (in which JFK Jr. was also allegedly a potential candidate). The official investigation determined the cause of the crash to be pilot error.

Is it possible to perform a statistical analysis?

Theoretically, yes, one could try to conduct a statistical analysis, but its results would be highly debatable and subject to many methodological problems, which would make them unreliable for proving a conspiracy.

Problems with the statistical analysis of the "Clinton body count":

   
• Lack of a clear "population": How to define "people associated with the Clintons"? This could range from a few dozen to hundreds of thousands (everyone who ever worked at the White House, was a political donor, an acquaintance, etc.). Without a clear definition of a comparison group, it is impossible to estimate "normal" mortality.
   
• Heterogeneity of causes of death: The list includes suicides, accidents, diseases, and murders. These events have different baseline probabilities.
   
• Age and health status: People associated with politicians are often adults or elderly, who are more likely to die from natural causes due to their age. The death of a 60-year-old from a heart attack is not statistically anomalous.
   
• "Large numbers": The wider a person's social circle (especially for presidents and influential politicians), the more people are in that circle, and the more deaths from natural causes there will be, simply due to the law of large numbers.
   
• Retrospective sampling and confirmation bias: The list is created after the events, selecting those that appear "suspicious." This is a classic case of confirmation bias, where evidence is sought to support an already existing hypothesis.
   
• Lack of a baseline probability: What is the "average" probability that, say, 50 people from a high-ranking politician's circle will die within 20-30 years? This is difficult to establish, as there is no comparable "control group" (e.g., deaths in the circles of other former presidents or senators) with a similar level of public scrutiny and conspiratorial attention.

What method could be used (if data were available):

   
• Poisson distribution: Suitable for modeling the number of rare events (deaths) over a specific time period in a fixed population. One could compare the observed number of deaths with the expected number, based on baseline mortality rates for that age group.
   
• Z-test or t-test: If we had sufficiently large and comparable samples, one could compare the average mortality rates or the proportion of "suspicious" deaths between the groups.
   
• Regression analysis: In a more complex model, one could try to account for factors such as age, health status, profession, etc.

Conclusion on statistical analysis:

In practice, official and independent investigations have found no evidence that the deaths on the "Clinton body count" list are the result of crimes related to the Clintons. Most of these deaths have been explained by natural causes, suicides, or accidents, and claims of a conspiracy are often based on speculation and misinterpretation of facts.

A statistical analysis conducted without a strict methodology can easily lead to false conclusions. In a situation where there is no clear "control group" and no real statistics on "suspicious" deaths around other politicians, and the list is compiled with bias, any "statistical" conclusion will be extremely vulnerable to criticism. The scientific community, for the most part, rejects the idea that the number of deaths around the Clintons is statistically anomalous or proves a conspiracy.

You've raised a very important question about the statistical analysis of unusual events, especially when they involve suspicious deaths. In the case of John Barnett and Joshua Dean, such an analysis could indeed be performed, but with several caveats that are critically important for interpreting the results.

1. Is it possible to apply statistical analysis to the deaths of Barnett and Dean?

Yes, theoretically it is. The goal of such an analysis is to assess how "accidental" such a confluence of circumstances is by comparing it with the expected probability.

Steps for an approximate statistical analysis (hypothetical):

Defining the "event":

   
• Event 1 (Barnett): The death (alleged suicide) of a high-profile "whistleblower" from a major corporation, who was actively involved in a lawsuit against it, at the age of 62.
   
• Event 2 (Dean): The death (from a rapidly progressive infection) of another "whistleblower" (a supplier to the same corporation), who also had claims against the company, at the age of 45, shortly after Event 1.
   
• "Connection": Both were associated with criticizing Boeing/Spirit AeroSystems, both were actively speaking out against the companies, and both deaths occurred within a short period of time (less than 2 months).

Gathering baseline probabilities:

   
• Probability of suicide: The average suicide rate in the U.S. for men aged 60-64. (For example, according to CDC data, this could be around 25-30 per 100,000 people per year).
   
• Probability of death from an aggressive infection (e.g., MRSA): This is more complex, as it depends on overall health status, access to medical care, geographical region, etc. But one could take the general incidence and mortality from severe bacterial infections for men aged 40-49. (This rate would be very low for a healthy person, as Dean was described, but higher for the general population).
   
• Probability of being a "whistleblower" at a major company: Extremely low.
   
• Probability that a whistleblower is actively involved in a lawsuit: Even lower.
   
• Probability that two such people die within a short period: This is the key point.

Formulating the null hypothesis (H0​):

The deaths of John Barnett and Joshua Dean are statistically independent events that occurred due to natural or random causes, and their temporal coincidence is a matter of chance.

Calculating the p-value (approximate approach):

This could be done by multiplying the probabilities of the individual events to get the probability of their joint occurrence.

Simple model: P(Barnett's death) × P(Dean's death ∣ Barnett's death and connection).

P(Barnett's death): the probability of suicide for a 62-year-old man, plus, possibly, a multiplier for being a "whistleblower" involved in a lawsuit (which can increase stress and risk).

P(Dean's death ∣ …): the probability of a 45-year-old man's death from an aggressive infection, given that he is also a whistleblower connected to the same company, and this occurs shortly after the first death.

Key difficulty: Accurately defining these baseline probabilities and their independence/dependence. For example, if the stress from a lawsuit increases the risk of suicide, this should be taken into account. If there is a general "culture of intimidation" within the company that affects the health of whistleblowers, the events may be indirectly linked.

Example (very simplified and hypothetical):

Let's assume the annual probability of suicide for a man aged 60-65 is PS​ = 0.0003 (0.03%).

Let's assume the annual probability of death from a highly aggressive infection for a healthy man aged 40-45 is PI​ = 0.00005 (0.005%).

Let's assume there are N "active whistleblowers" at a major corporation (the number is very small, say, 10-20 people per country at any given time).

The probability that one of them dies in such a way is quite low.

The probability that two such people die within 2 months (about 1/6 of a year), given their "connection" and "whistleblower status," will be:

P ≈ PS​ × PI​ × (proximity in time factor) × (whistleblower status factor)

Without precise data on the number of "whistleblowers," their average age and health status, as well as baseline rates for this specific group (people under severe stress from lawsuits against major companies), it is impossible to calculate an accurate p-value.

Limitations and interpretation of results:

   
• Small sample size: Two deaths are a very small sample for statistical analysis. Any p-value obtained from such a small sample will have very wide confidence intervals and a high degree of error.
   
• Sample bias: We are talking about specific people in unique circumstances (whistleblowers). Their risk of suicide or illness may be higher due to stress, persecution, or pre-existing health issues that were exacerbated by the situation.
   
• The nature of "conspiracies": If it is a "conspiracy," the events are not random but targeted. Statistics are powerless in this case. They can only show how "unusual" the coincidence is, but not prove the cause.
   
• Reputation and bias: The public nature of these deaths and pre-existing conspiracy theories can influence perception. People are prone to finding patterns where there are none, especially when there is an element of drama or injustice.

In the case of Barnett and Dean, although the official causes of death are different, their unusual confluence of circumstances (both whistleblowers, associated with the same company/supplier, died within a short period) raises serious suspicion and questions from the public and the media. A statistical analysis, if it could be conducted with perfect data, would likely show a very low p-value (i.e., a very small probability of such a random coincidence), which would indicate that it is not simply a matter of chance.

However, even a low p-value does not prove that it was murder or a deliberate "elimination." It merely indicates that the observed event is statistically unlikely under the null hypothesis of complete randomness. This prompts further investigation but is not in itself proof of a crime or conspiracy. It is more of a "red flag."

Conclusion:

The application of statistical analysis to such cases is possible but is fraught with great methodological difficulties due to the lack of precise data and the complexity of determining a "baseline probability" for such specific circumstances. Public suspicion in these cases stems not only from statistics but also from the context: the status of the deceased as "whistleblowers," their connections to a major company, lawsuits, and the general history of corporate malfeasance.

Using a "normal pair of people" or "a pair of men" as a control group for the statistical analysis of the deaths of John Barnett and Joshua Dean (or other "suspicious" deaths) is not possible, because such a control group would be unrepresentative and methodologically incorrect.

Why a "normal pair of people" is not a suitable control group:

Lack of specific context

   
• Whistleblower status: Barnett and Dean were not just "normal people." They were "whistleblowers" who were actively speaking out against a major corporation, participating in lawsuits, and likely experiencing immense stress, pressure, and potential persecution. This is an extremely rare and stressful status. A "normal pair of people" is not subjected to such pressures.
   
• Connection to a major corporation/supplier: Their problems were directly related to their jobs and a specific company (Boeing/Spirit AeroSystems).
     
• Publicity and significance: Their deaths attracted enormous public attention precisely because of their status.

Different risk factors

   
• Psychological pressure: Whistleblowers often face harassment, threats, dismissal, and lawsuits – all of which significantly increase the risk of mental health problems (including depression and suicidal thoughts) and physical stress.
   
• Potential threat: Even if external interference is not proven, the very fact that a person possesses "sensitive" information and speaks out against influential structures can create unique risks (although this goes beyond a purely statistical analysis).

Low baseline probability

The events we want to analyze (the death of a "whistleblower" in such a context) are themselves extremely rare. If we compare them to the general mortality of "normal people," we will get a very low p-value, which, however, will be a false positive because we did not account for the unique risk factors of "whistleblowers."

How to conduct a more accurate analysis (difficult, but possible)

To conduct a more accurate (though still difficult) statistical analysis, the control group must be as similar as possible to the cases being studied:

   
• An ideal (but unrealistic) control group: Other "whistleblowers" from major corporations who were actively involved in lawsuits but did not die, or died from confirmed natural causes, and who were not under such close scrutiny. However, assembling such a group and tracking its mortality would be extremely difficult.
   
• The best of the realistic approaches: Compare the mortality in the "whistleblower" population with general mortality, but also include statistical adjustments in the model for known risk factors (age, gender, possibly stress level, mental health, etc.). Even in this case, due to the small sample size (only two deaths), the conclusions would be more "suspicious" than "conclusive."

Conclusion

Using a "normal pair of people" as a control group would lead to a statistical error because it does not reflect the unique circumstances and risk factors associated with the status of "whistleblowers" speaking out against powerful corporations. It would be an apples-to-oranges comparison, and the resulting low p-value would have no real significance in confirming or refuting the hypothesis of the "non-accidental" nature of these deaths in their specific context.

Such cases will always be in a zone of heightened attention and speculation precisely because they deviate from "normal" statistics and raise questions for which the public seeks answers.

You are asking to recalculate the probability for two specific men (Barnett and Dean), assuming they are "normal" men of their age but with an increased risk due to severe stress. This approach differs from the "whistleblower pool" model and focuses on the individual, rather than group, probability of coincidence.

Statistical analysis for two "normal" men under stress (Barnett and Dean)

Let's calculate the probability that two specific men (Barnett and Dean), given their age and assumed stress, would die for the specified reasons within a short period of time.

Our assumptions:

John Barnett (62 years old, suicide):

   
• Baseline annual probability of suicide for men in the U.S. aged 55-64: approximately 28 per 100,000 people (0.00028).
   
• Stress correction: You suggested increasing the risk by a factor of 10 due to severe stress (like that of a whistleblower in a lawsuit).
   
• Adjusted annual probability: 0.00028 × 10 = 0.0028.
   
• Probability of death within 2 months (1/6 of a year): P1 = 0.0028 / 6 ≈ 0.000467.

Joshua Dean (45 years old, rapid aggressive infection):

   
• Baseline annual probability of death for a healthy man aged 40-49 from a very rapid and aggressive bacterial infection leading to organ failure: This is a very rare event. Let's use a very low estimate of 1 in 200,000 people (0.000005).
   
• Stress correction: Let's apply the same 10-fold multiplier, although the effect of stress on such an infection is less straightforward than on suicide.
   
• Adjusted annual probability: 0.000005 × 10 = 0.00005.
   
• Probability of death within 2 months: P2 = 0.00005 / 6 ≈ 0.00000833.

Calculating the p-value:

We are looking for the probability that these two specific men would die in the specified manner within 2 months, assuming these events are independent.

p = P1 × P2

p = 0.000467 × 0.00000833

p ≈ 3.89 × 10−9

Interpretation:

The resulting p-value is approximately 3.9 × 10−9, which is equal to about 1 chance in 250 million.

What does this mean? Even assuming that these two men were under stress that increased their individual risks of death from these causes by a factor of 10, the probability of such a coincidence (one dying by suicide, the other from a rare infection) within 2 months for these two specific individuals is extremely low if the events were random and independent.

Comparison with the previous model: This result is even lower than in the previous "whistleblower pool" model. This is because here we are not dividing the probability by the number of possible pairs but are focusing on the probability of these two specific individuals.

Key limitations:

   
• Strong dependence on assumptions: As before, the result is entirely dependent on our initial, very rough assumptions about baseline probabilities and, especially, the stress factor (10x), which is arbitrary.
   
• Does not prove interference: A low p-value only shows that the coincidence is statistically unlikely under the null hypothesis of randomness. It does not prove intentional interference but may indicate that there is something "non-random" in the circumstances of the deaths that requires further investigation (e.g., the effect of stress on health or other, more complex relationships).

A very simple statistical analysis for the "Clinton body count" list

As I've already explained, creating an accurate statistical model for the "Clinton body count" list is extremely problematic due to the lack of clearly defined data and the subjectivity of the list itself. However, I can offer a very simplified approach to illustrate how one might think about a "cluster of deaths" around a public figure.

Goal:

To estimate how likely it is that an observed number of "unexpected" deaths would occur around a high-ranking politician over a long period if it happens by chance.

Our Assumptions (extremely simplified):

   
• Size of the politician's "inner circle": Let's assume the politician's "inner circle" (including staff, friends, donors, opponents who frequently interact or about whom there is public data) consists of 1000 people over 30 years of his active public career. (This number is larger than in the previous analysis to cover the wider circle mentioned in conspiracy theories).
   
• Time period: 30 years.
   
• Definition of an "Unexpected Death": This is any death that is not a natural death in old age. For simplicity, let's combine suicides, accidents, murders, and any unexpected deaths from diseases in people under 70.
   
• Baseline annual probability of an "unexpected death": Let's assume a very, very low overall probability of such an event for any random person in any given year. Let's say, 1 in 10,000 people (0.0001) per year. (This is a very low estimate for all these categories, but it is needed to show that an "unexpected" death is relatively rare).

Calculation (using the Poisson distribution):

The Poisson distribution is well-suited for modeling the number of rare events (deaths) over a specific period of time in a given population.

Expected number of "unexpected deaths" (λ):

λ = (Circle size) × (Probability of "unexpected death" per year) × (Number of years)

λ = 1000 × 0.0001 × 30 = 3

That is, in our simplified scenario, we would expect 3 "unexpected deaths" in a politician's circle of 1000 people over 30 years, if everything happens by chance.

Observed number of "unexpected deaths" (k):

Conspiracy-based "Clinton body count" lists often include 40-50 or even more deaths. Let's take k = 40 for our analysis.

Calculating the p-value:

We want to find the probability that with an expected number of λ = 3, we actually observe k = 40 or more "unexpected deaths."

P(X ≥ 40 ∣ λ = 3)

This value will be astronomically small, practically equal to zero. If you used an online Poisson calculator, it would likely show "0" or a number like 10−30 or less.

Interpretation Part 2:

   
• Very low p-value: The resulting p-value (practically 0) means that observing 40 "unexpected deaths" in a circle of 1000 people over 30 years is extremely unlikely if these deaths are random and independent events occurring with a baseline probability of 1 in 10,000 per year.
   
• Statistical anomaly, but not proof: This result shows that, according to our simplified model, the observed number of deaths is a statistical anomaly. This confirms why such "lists" arouse strong suspicion in those who believe in them.

Key LIMITATIONS and IMPORTANT CLARIFICATION (especially for the "Clinton body count" list):

   
• The "Clinton body count" list is not a scientific sample. It is compiled with bias, retrospectively, from public sources, often without considering all the circumstances of the deaths and without comparison to a control group (e.g., the "inner circle" of other former presidents).
   
• Subjectivity of an "Unexpected Death." Many deaths on such lists are officially explained by natural causes, which only become "suspicious" in the context of a conspiracy theory.
   
• Does not prove a conspiracy. This analysis is NOT proof of the existence of a conspiracy or the involvement of the Clintons in any deaths. It merely demonstrates that if you accept the assumptions that lead to a low expected random mortality and you see the observed number of deaths, this coincidence will be statistically unlikely.

Such statistical exercises help to understand the logic of those who see "conspiracies" in random events, but they cannot replace thorough investigations and evidence.

In the previous responses, we tried to apply statistics to very complex and sensitive topics. Now let's break it down simply so that even a person without mathematical experience understands what we did and why it's necessary.

What is a p-value in simple terms?

Imagine you toss a fair coin 10 times. You expect heads to come up about 5 times. But what if it comes up 9 times? That seems strange, doesn't it?

A p-value is a number that tells you how "strange" or "unusual" your observation is, if in fact nothing strange was happening (i.e., if the coin were fair, or if the deaths were just a matter of chance).

   
• If the p-value is small (e.g., less than 0.05 or 0.01): This means that your observation (9 heads out of 10) would be very, very unlikely if everything were "normal" (the coin is fair). It's as if the coin is screaming at you: "Hey, something's not right here! I'm probably not a fair coin!"
   
• If the p-value is large (e.g., greater than 0.05): This means that your observation isn't all that unusual. It could have happened simply by chance, even if everything were "normal."

In the context of our cases:

We asked: how likely is it that such a coincidence of deaths (Barnett and Dean), or such a number of "unexpected" deaths (the Clinton list) would happen simply by pure chance, without any hidden interference or unusual causes?

A very small p-value in our calculations (for example, 1 in 250 million or 1 in 95 thousand for Barnett/Dean, and practically 0 for the Clinton list) means: "If these deaths were just a matter of chance, the probability of such a coincidence would be extremely, extremely low."

Important to remember:

A small p-value doesn't prove that something was rigged or that a conspiracy exists. It simply says: "This is so unusual for a random event that it's worth taking a closer look. Maybe there's some non-random reason we didn't account for, or our idea of what's random was wrong."

Explanation of the Barnett and Dean analysis (for someone without mathematical experience)

Imagine you want to understand why two people who both complained about the same company died so quickly, one after the other.

How we reasoned:

   
• Who are our "suspects" (in terms of unusualness)? Two people – John Barnett (62, died by suicide) and Joshua Dean (45, died from a rare and rapid infection). Both were "whistleblowers" against Boeing/its supplier.
   
• How did we define "normality" for each?        
             
  • For Barnett: We took the suicide statistics for men his age. This is our "normal" rate.
             
  • For Dean: We took the statistics for deaths from the same rare and rapidly progressing infection for men his age. This is our "normal" rate for such a rare event.
         
     
   
• Accounting for "special circumstances" (stress): We understand that being a "whistleblower" is an immense stress. We assumed that this stress could increase the probability of both suicide and such an unusual illness by a factor of 10 (this is an arbitrary number, but it shows that we tried to account for the special conditions).

The question: What is the probability that these two specific people, given their ages, causes of death, and the "10x stress," would die like this, within 2 months, simply by chance?

The result: We got a very, very small p-value (approximately 1 chance in 250 million).

What this means in simple terms:

Imagine a lottery. If you bought one ticket, the chance of winning is 1 in 250 million. That is practically unbelievable. Our p-value says that the coincidence of these two deaths is just as (or even more) unlikely as winning such a lottery, if everything were "fair" and just by chance.

This makes one wonder: either it is a very, very rare random coincidence, or there are some non-random factors we didn't account for (e.g., actual external interference, or stress affects the body much more strongly and specifically than we can measure, or these deaths were not as "random" and "independent" events as we assumed).

Explanation of the "Clinton body count" analysis (for someone without mathematical experience)

The situation here is more complicated because the "Clinton body count" list is not strict data but a collection of various deaths that was initially compiled by people who believe in a conspiracy.

How we reasoned (hypothetically):

   
• What are we comparing? We want to compare the number of "unexpected" deaths in a politician's "circle" with how many such deaths would have occurred from purely random causes.
   
• Who is in the "circle"? We took a very large number, for example, 1000 people who were "close" to the Clintons over 30 years (staff, friends, major donors, etc.).
   
• What is an "unexpected death"? We simplified it greatly: it's any death that is not a death from old age (i.e., suicide, accident, murder, or simply a sudden, unusual death from illness in relatively young people — say, under 70).
   
• What is the probability of an "unexpected death" for an average person? We assumed it's a very rare event — for example, 1 chance in 10,000 per year.
   
• How many "unexpected deaths" do we expect? If everything were random, then in a circle of 1000 people over 30 years, we would expect only 3 such deaths (1000 people * 1/10000 * 30 years = 3).
   
• How many deaths do we see on the "Clinton body count" list? They usually list 40-50 or even more deaths. We took 40.

The result: We got a p-value that is practically zero.

What this means in simple terms:

Imagine you come to a party where only 3 people are supposed to win a prize by chance. But you see that 40 people win prizes! Your p-value says: "It's almost impossible for so many prizes to have been won if the prizes were given out randomly."

Again, this is not proof of a conspiracy!

This means that either:

   
• It's a truly incredible coincidence (very, very unlikely).
   
• Or our initial assumptions are wrong. For example:        
             
  • The politician's "circle" is much larger than 1000 people, and simply due to the huge number of people, more would statistically be expected to die.
             
  • The deaths on the list are not so "unexpected," and we have misclassified them (e.g., normal deaths from illnesses that conspiracy theorists label as "suspicious").
             
  • The list itself is compiled with bias, collecting only the "strange" cases and ignoring thousands of "normal" deaths.
         
     

How to perform such tests "for other cases/hypotheses"? (Example: "UFO disappearances")

Let's take a popular conspiracy theory: "UFOs often disappear right before they are about to be video-recorded or photographed, so that the government can hide their existence."

How can we "test" this using such a "test":

   
• Clearly define the "event" you are interested in: "A UFO is seen, and someone tries to film it, but the UFO disappears before it is filmed."
   
• Define the "null hypothesis" (i.e., that everything is "normal" and there is no conspiracy): "UFOs do not exist, and what people are seeing are planes, drones, natural phenomena, lens flare, and they disappear randomly or simply fly away."
   
• Gather (or invent for the example) data:        
             
  • How many times have people tried to film a "UFO"? Let's say 1000 times a year people report seeing something that looks like a UFO and tried to get their camera out.
             
  • How many times did the "UFO" actually disappear before being filmed? Let's say, out of these 1000 attempts, the "UFO" disappeared 950 times.
             
  • How many times were they successful in filming a "UFO" clearly? Let's say, 50 times.
         
     
   
• Estimate the "baseline probability" (if the "null hypothesis" were true):        
             
  • If people are seeing planes or drones, they might fly away or hide behind clouds by chance before they are filmed. Let's say the probability that an object (which is not actually a UFO) accidentally disappears before being filmed is, say, 80% (0.8). This is our "normal" probability if UFOs don't exist and there is no cover-up.
         
     
   
• Calculate the p-value (very simplified):        
             
  • We would expect that out of 1000 attempts to film a "UFO," it would disappear 1000 × 0.8 = 800 times.
             
  • But we observe that it disappears 950 times.
             
  • Now the question is: how unlikely is it to get 950 disappearances out of 1000 attempts if the expected number is 800 (and there is no conspiracy)?
             
  • This is more difficult to calculate without formulas, but the idea is: if 950 is very different from 800, and this happens many times, the p-value will be small. If 950 is close to 800, the p-value will be large.
         
     
   
• Interpretation:        
             
  • If the p-value turns out to be very small, it will say: "Wow, 950 disappearances out of 1000 is a lot if the objects were just flying away randomly. Maybe there's something else making them disappear (e.g., they are intelligent and don't want to be filmed, or there's someone who is covering them up)."
             
  • If the p-value is large, it will say: "Well, 950 disappearances are a lot, of course, but it could also have happened by chance if the objects are indeed flying away quickly."
         
     

Again, this does not prove the existence of UFOs or a conspiracy! It is merely a way to quantitatively assess how "unusual" an observed phenomenon is compared to what we would expect if everything were "normal." This can prompt further investigation but is not final proof.