Jay Bonnar’s Anecdote is Statistically Impossible If the COVID Vaccines are Safe

Jay Bonnar’s Anecdote is Statistically Impossible If the COVID Vaccines are Safe

By STEVE KIRSCH

Jay lost 15 of his friends who all “died suddenly.” All were vaccinated. Four dropped dead within 24 hours of the shot. 3 of the 4 were ~30 years old, perfectly healthy before their death. Whoa.

High-tech sales executive Jay Bonnar, age 57, had 15 of his direct friends (not “friends of friends”) die unexpectedly since the COVID vaccine rolled out. All his friends who died were vaccinated. In his entire life, he’s never had any friends die unexpectedly. Zero. If the vaccines are safe, the chances of this happening are near zero. In other words, Jay’s experience is proof that the vaccines are causing people to die unexpectedly. Think I’m wrong? Where is your anecdote showing that the unvaccinated are dying unexpectedly at the same rate as the vaccinated? Surely, those anecdotes should be TRIVIAL to find, right?

Executive summary

This is simply untrue. In fact, it is misinformation.

A single, independently-verifiable anecdote can be extremely powerful. It can totally destroy the scientific consensus and prove beyond any reasonable doubt that the CDC is lying.

I’m going to show you an example of this in this article.

One guy whose verifiable story completely blows the “safe and effective” narrative.

I’ll show how we can apply various statistical analyses to Jay’s story to show convincingly that what Jay observed cannot happen by chance and cannot happen if the vaccines are as safe as the FDA claims.

The only way to explain Jay’s story is that the COVID vaccines are killing around 1 person per 1,000 doses as I’ve said before.

Jay’s story aligns with my estimate and is statistically impossible if the vaccines are as “safe” (as the FDA has claimed).

Jay’s story at a glance

1. Jay is a 57 year old sales executive in Seattle, WA
2. Jay has 7500 direct friends that he knows personally; he estimates 75% have taken the COVID vaccine: 5,625 vaxxed and 1,875 unvaxxed.
3. 0 unexpected deaths in Jay’s history prior to the vax rollout; this includes during COVID, but before the vaccine.
4. Since the COVID vaccines rolled out, Jay has lost 15 friends, all vaccinated, who died “unexpectedly.” None of his unvaccinated friends died.
5. 4 of his 15 friends who died died within 24 hours of their COVID vaccine. I’d call this a smoking gun. It’s a clue as to what might have killed them.
6. 3 of the 4 “same day” deaths were in people who were 30 years old
7. None of them had COVID at the time they died or just before they died. Everyone who got vaccinated and died had COVID at least once. Jay estimates his vaccinated friends are getting COVID at a rate easily 4X more than his unvaccinated friends.
8. Jay estimates he has roughly 7,500 friends (email contacts, LinkedIn contacts, etc). These are all direct relationships where Jay has spoken directly to that person.
9. Before the COVID vaccines rolled out, Jay had never, in his entire life, known anyone who died unexpectedly. This means when he heard of the death, he would react as “wow, that was totally unexpected.” So someone who is involved in a traffic accident isn’t unexpected because accidents happen. Someone who has a long history of heart disease who dies from heart disease isn’t unexpected. Someone who is very old dying from natural causes or an accident isn’t unexpected. An unexpected death means the person was fine the day before they died and then, was dead the next day. For example, a 20 year old who is totally fit dies in his sleep. Or a 40 year old develops a “turbo cancer” and dies a couple of months later.
10. Jay is the very first person I’ve talked to who personally knew more than 10 people who died unexpectedly and who was willing to disclose their names so that third party fact-checkers could verify the information was true. This makes his story unique. So Jay wasn’t cherry picked from thousands of anecdotes. He was simply the first person who spoke to me who had a large enough sample to be statistically interesting and who could reveal names so that his story could be verified.
11. However, even if I talked to everyone in the world, I wouldn’t be able to find anyone with Jay’s story if the vaccines were perfectly safe and not causing unexpected deaths (I’d have to chat with at least 2e21 people to find such a person which is 12 orders of magnitude more people than on planet Earth).

The interview

Key takeaways

1. The COVID vaccine is killing people at the rate of 1 per 1,000 doses. Jay’s data is exactly aligned with this. There were 14,000 estimated doses among his vaccinated friends and 15 of them died unexpectedly.
2. The CDC and FDA are lying to you by claiming the COVID vaccine is safe (which means kills 1 in 1M). The probability the FDA is right is poisson.sf(14, .014)=1.2e-40 which basically says there is no way the FDA is right (14,000 doses should yield .014 deaths per the FDA, and Jay observed 15; we have to subtract 1 because the survival function is >X rather than >=X).
3. There is no other way to explain away this data. For example, even if Jay is “more aware” of deaths now which accounts for the dramatic rise in unexpected deaths he observed post-vaccine, the sheer number of unexpected deaths Jay experienced relative to the size of his friend network make this an anecdote that cannot be dismissed with hand-waving arguments about heightened perception; it just means that one of the many mathematical analyses (rate before COVID vs. after COVID) could be attacked.
4. The rate of unexpected deaths is probably around 100X higher than it was before the COVID vaccines (which was very small). Virtually all the increase in this category has been driven by the COVID vaccine. So, for example, in the last 55 years, Jay never had an unexpected death. But say he’s only noticing this since age 35. So 20 years, no events. Now he’s averaging 6 per year. So if he had one death in the 30 years, that would be a 120X increase.
5. Note that athlete sudden deaths increased by just 19X (538/yr vs. an average of 29 per year per GoodSciencing.com), but there was a baseline of 29 expected deaths per year; the number of unexpected deaths (where people just died unexpectedly was likely <20% per year so 6 deaths that were truly unexpected, so the net new number of unexpected deaths is 509/yr vs 6/year is nearly a 100X increase).
6. The only way to explain Jay’s evidence is that the COVID vaccine caused the 15 excess deaths.

Jay Bonnar saw a lot more black swans than is humanly possible if the CDC is telling the truth

There is no way that the CDC can gaslight this by pointing to studies claiming that there are a small number of black swans out in the wild.

It happened and it’s verifiable: Jay saw 15 black swans.

In fact, it’s even worse. Four of the people who died were double-black swans (died on the same day as the vaccine); these are 180X rarer than just black swans!

The CDC essentially said the vaccines are safe which means they kill fewer than 1 person per million doses. So Jay’s vaccinated friends, with 14,000 doses, should have experienced .014 deaths according to the CDC, but instead ended up with over 1,000 times that number of deaths. The chance of that happening just by random “bad luck” to Jay is 1.2e-40 for Jay to see 15 or more black swans (this is given by the survival function poisson.sf(15-1, .014)).

Jay’s story is basically unexplainable if the CDC is telling the truth and the COVID vaccines are perfectly safe.

The CDC is lying. The COVID vaccines are unsafe. It’s a mathematical certainty.

In short, Jay saw way too many black swans for us to believe the CDC that black swans are really as rare as they claim.

Anyone can verify his anecdote (because he lists all the names) and anyone can do the math.

Conversely, based on my estimates of 1 death per 1,000 doses, we get poisson.sf(14, 14)=0.42956328717262765 which means my explanation is perfectly reasonable whereas the FDA’s is statistically impossible.

If I’m wrong, simply explain how Jay could see so many black swans among his 7,500 friends.

How I met Jay

Jay’s friends who have died

Died within 24 hours of COVID vaccination (4):

1. Scott Plutko, 53, – work colleague, SVP of Global Channels, Saviynt; talked of getting boosted “to do the right thing” and 24 hours later had a massive heart attack and dropped dead in front of a live audience in London during a tech presentation. Proof he died

   

2. Alexander Nuber, 30 y.o. Man, worked out at my gym, perfectly fit and  healthy. We spoke in person about how eager he was to get the C19 injection. He received it and four hours later dropped dead at home. Doctors were baffled. He died October 17, 2021. Here is his obituary. There is no mention that he died on the same day he was vaccinated. Are you surprised? The COVID vaccine is the killer that few people have enough courage to blame.

   

3. Zach Broten, 30, personal trainer, worked at my gym. Had a heart attack on the same day as the shot and died just days before his 31st birthday. He told other people at the gym he had just received the C19 injection.

   

4. Andrew Titus (nephew) – 28, perfectly healthy collegiate lacrosse athlete, died suddenly in sleep right after getting the shot; had received both injections and several boosters. Doctors were baffled as to the cause. Here’s his obituary. No mention he died on the day he got vaccinated

   

Other unexpected deaths (11)

1. Alejandrino Taculad (former father-in-law), 78, received injections and boosters. 60 days later was diagnosed with stage 4 lung cancer. Never smoked or drank alcohol a day in his life; died 90 days after getting the jab.
2. Matt Runte, 44, Seattle firefighter; I knew Mark through friends in the first-responder community. Mandated to get the jab. Was out for a jog and just dropped dead.
3. Jessica Wilson, early 30’s  – Seattle mom, my kids attended the same school where hers did; couldn’t volunteer as room mom w/out the jab per Gov Inslee mandate, blood clots in lungs weeks after injection and died suddenly one week later.
4. Dori Monson, 61, radio talk show host, met through friends – died of “cardiac event”; he was vaccinated and spoke of his regret for getting it prior to death.
5. Chloe Nuttbrock, 18, Mukilteo HS student – aneurysm, daughter of a friend/neighbor, vaccinated. Had migraines after covid vax. Died 1 week later.
6. Gabriel Jungmann, 20, of Bellevue – died suddenly while showering; vaccinated.
7. Chris Smith, 31, heart attack – XFL athlete, went to my church, vaccinated per league requirements.
8. Rachel Marshall, 42, owner of Rachel’s Ginger Beer – died suddenly (cardiac arrest). I was a customer at her shop (but stopped going after they required vaccines). Vocal vaccine and mask proponent.
9. David Black, 55, work colleague (Boeing), died suddenly (cardiac arrest); no known health problems; Boeing has mandatory vax requirement.
10. Steven Hahn, 54, church member, died suddenly and unexpectedly from “heart problems” – wife said he had been vaccinated. Nobody can explain this. No known health problems. Super-healthy guy.
11. Michael Howland, early 40’s, died from a heart attack just walking on the beach while on vacation in Cancun, Mexico with family, super fit. Triathlete. Company had a C19 injection mandate.

Injured (7)

• Rosalinda Taculad (former mother-in-law), 70, had a massive stroke just 1 week after her second injection. She is permanently disabled now; her husband Alejandrino died 120 days after his injections.
• Hannah, friend of my wife – 25, perfectly healthy, stroke and paralysis, permanently disabled a few months after getting the jab. Told me how excited she was to get the C19 vaccine so she could “get on with life.” Refuses to admit it was the vaccine. Believes she was injured because she was working too hard (50 hours/week). Doctors baffled as to how a 25 year-old could be disabled. Her vertigo is so bad that she can’t work. Tinnitus. Paralysis. Constant pain. Thousand needles. No relief.
• Steve, former manager at work – VP Sales, 52, heart attack 1 week after his booster; Steve was a vocal advocate of the injections, tried to convince me to take it “to do the right thing” and stated everyone in his family would be vaccinated (see below).
• Steve’s 16 yo daughter – HS soccer star, full scholarship to Univ of UT, heart attack after booster and permanently disabled; had to have a pacemaker installed for the rest of her life. Her heart attack happened while Steve was in the hospital recovering from his heart attack.
• Steve’s daughter’s 16 year-old boyfriend – HS football star, heart attack after 2nd injection, can never play sports again. The boyfriend had his heart attack 2 weeks after Steve’s daughter. All three of them (Steve, daughter, boyfriend) all went to the same doctor to get injected together. They all had cardiac injuries within 1 to 2 weeks of each other.
• Ryan, friend in Canada – His perfectly healthy 16 y.o. daughter had a heart attack just one day after she got the booster. Diagnosed with Type 1 diabetes one month post injection. Previously perfectly healthy. Now has an implant in her kidneys. She’s now 17. The parents divorced over this incident.
• Gary – 52 y.o. Man, perfectly healthy and extremely fit. MMA fighter. Runs a security company in the Seattle area. Didn’t want to do it, but was forced to get jabbed by his employer. Gary got the shot at noon and that night had a heart attack. Had to be rushed to the ER. After 3 weeks, he had to get the second shot to remain employed. Today he can’t exercise. Can’t do a push up or walk up a flight of stairs. He can no longer work.

Last names in some instances are withheld since Jay doesn’t have family permission to speak about them. The rest of the injured are 2nd hand accounts (over a dozen), friends of friends, and I don’t have last names or details other than the victims being mRNA injected and are severely or permanently disabled, cancer, diabetes, etc., with no previous medical issues pre-injection.

Analysis by Professor Norman Fenton: there is only a 1 in 81 million chance the vaccine is “safe” (i.e., doesn’t raise the mortality rate)

He only uses the top-level observations.

Professor Fenton did NOT use the fact that so many people who died were young and that all were unexpected deaths. So his estimate is a LOWER BOUND. This means that accusations that I simply cherry picked this example from a the hypothetical100M anti-vaxxers who follow me is false since even if I did that I couldn’t find a story this bad.

Evidence used: 7500 friends, 15 dead, 75% were vaccinated, only vaccinated died, none of the unvaxxed died, and 4 of the vaccinated died on the same day as the shot was given. He did NOT use their ages, medical history, and the fact that all deaths were unexpected. He didn’t use temporal proximity of any of the 11 deaths to the date of the shot.

Conclusion: the combined evidence results in a probability of only 1 in 81 million that the vaccine is safe (i.e., that the vaxxed mortality rate is no greater than the unvaxxed mortality rate).

Here’s why:

Let H be the hypothesis: “vaxxed mortality rate is no greater than the unvaxxed mortality rate” (i.e. vaccine is safe)

By Bayes if we start with the assumption that P(H)= 0.5 (i.e. 50%) (strictly speaking we assume the mortality rates of the vaxxed and unvaxxed are uniformly distributed between 0 and 100%) then if we observe 0 deaths from 1875 unvaxxed and 15 dead from 5625 vaxxed the posterior probability of H becomes 0.0102 (i.e. 1.02%)

So we now have a revised probability P(H)=0.0102

But Let E be the evidence of at least 4 out of 15 deaths on day 1 of the vaxxed.

Now if H is true we know that the probability of observing a death on day 1 (of the 182 days) is 1/182 which is 0.0055.

Using the Binomial theorem the probability of at least 4 deaths out of 15 on day is approx 0.0000012 (about 1 in 833,333)

So we know that P(E given H) = 0.0000012

By Bayes theorem

P(H given E) = P(E given H)*P(H) / [ P(E given H)*P(H) + P(E given not H)*P(not H)

But we know P(H) = 0.0102 and P(E given H) = 0.0000012

We can also assume P(E given not H) = 1 and we know P(not H) = 0.9898

So plugging these values into Bayes Theorem gives a result of the posterior probability of H

P(H given E) = 0.00000001237

Which is about 1 in 81 million.

Poisson analysis: Jay saw 15 deaths, but the CDC said there should only be .014 deaths among his friends

>>> poisson.sf(14, .014)
1.1741428985146716e-40

In other words, the CDC is lying. If the CDC were telling the truth, Jay’s observations are “statistically impossible.” In other words, there is no way the vaccine is as safe as the FDA/CDC claims. Not even close!

That’s the most important part of this analysis: we can conclusively prove the FDA and CDC are lying.

Could the vaccine not be as dangerous as I think? Could it only be killing, say, 1 person per 10,000 doses (i.e., 10X lower than I estimate)? Let’s find out:

>>> poisson.sf(14, 1.4)
3.2132609254362016e-11

Nope! This is quite stunning.

It’s basically a certainty that the COVID vaccines kill at least 1 person per 10,000 doses at a bare minimum.

Which means that the vaccines aren’t even close to being safe.

And it means that giving a vaccine to a child who has a 1 in 1M chance of dying is insane.

You’ll easily be killing 100 kids or more for every kid you might potentially save if the vaccine worked.

Poisson analysis: unexpected death rate pre-vax vs. post-vax

Let’s estimate the rate of unexpected deaths in Jay’s life. Suppose he wasn’t paying attention to this until age 35. Also the death rates of his friends should double every 10 years.

He had zero deaths until age 55. So let’s say we call this 1 death in 10 years to be conservative.

So that is .25 deaths in a 2.5 year period.

So to see 15 or more deaths in 2.5 years would be:

>>> poisson.sf(14, .25)
5.634558720450966e-22

which is of course never going to happen. This means that Jay’s observations of sudden deaths didn’t happen by chance.

Could it be that all of Jay’s friends are anti-vaxxers and making him aware of sudden deaths that he would have been aware of before if they were paying attention pre-vaccine? Yes, it’s possible and this makes this analysis the most suspect since “heightened awareness” can influence the outcome. But unexpected deaths are fundamentally just that: unexpected and would be expected to happen at a very low rate in normal times.

Poisson analysis: 4 deaths within 24 hours of the vaccine

We have 15 deaths within a 6 month period between vaccines (to make life easier in our estimate). We can think of the 6 month period as days after vax.

We expect to see 15/180= .083 deaths in a day on average after a vaccine shot assuming that people get vaccinated every 180 days and 15 people died.

Given that average daily rate of deaths, the chance of 4 or more deaths within a day is given by the survival function:

>>> poisson.sf(3, .083)
1.8505637692983056e-06

which is very unlikely. But since we didn’t factor in the other key factor (all the deaths were in the vaccinated group), this is just a lower bound estimate.

This again confirms that the COVID vaccines caused the deaths of his friends within 24 hours of the shot; Jay didn’t get “unlucky.”

Fisher exact test analysis

This is using people rather than person-years to make it easy.

We had 5,625 vaxxed with 15 deaths and 1,875 unvaxxed with no deaths.

A Fisher exact test on this gives:

p=.013

95% CI for the odds ratio(1.197265283117275 to infinity)

which means we are 95% certain that the vaccine is elevating deaths and that there is only a 1% chance this result happened by random chance.

So of all the tests, this is the weakest because it makes the fewest assumptions.

How do you explain the Pfizer Phase 3 trial where only 21 deaths in the vaccine group and 17 in the placebo group

Not necessarily. See this explanation.

Jay isn’t an isolated example

Not surprisingly, I got a number of stories similar to Jay’s. No one had a story where the unvaxxed were dying unexpectedly at the same as the vaccinated. That in itself is telling. There are simply no cherries to pick on the opposite side!!!

What I got were stories consistent with what Jay observed:

After I DM’ed him, one of the messages he sent me was this:

More responses (you can click on each response to see the original post and replies):

There’s a very consistent pattern here. Can you see it? It’s the vaccinated who are mysteriously dying, not the unvaccinated.

Anecdotes supporting the narrative

Guess what? Not a single person with statistic showing “unexpected” deaths higher in the unvaccinated vs. vaccinated.

And my followers would be the best place to find such stats because they would have a higher portion of unvaccinated friends (so the cohort sizes would be more comparable to make a fair rate comparison). I’d want to a minimum 5 or more unexpected deaths in both cohorts to have a meaningful comparison to show that the rates are comparable in the two cohorts (in Jay’s case, proving the rates are not comparable simply required 10 or more incidents, but if you want to show the rates are comparable, you’d want to have 5 or more per cohort).

How can we be so sure it wasn’t COVID causing these unexplained deaths

1. None of his unvaccinated friends died unexpectedly (and Jay is not alone) in the entire 3.5 year period since 2000.
2. None of his friends died unexpectedly in 2020 (post-covid, pre-vaccine).
3. COVID isn’t a factor in any of the stories, but the vaccine is common to all of them.

Could I have collected 81M stories and just chosen the worst story to publish?

Also, even if his story was cherry picked from 100M stories, it doesn’t change the fact the CDC/FDA’s claim that the vaccine is safe is a lie. Even if the story was cherry picked, it’s still statistically impossible for the vaccine to be killing only 1 person per million doses or less (the definition of a safe vaccine).

How could Jay possibly know the status of so many people?

There are undoubtedly more deaths he doesn’t know about.

There is a heightened awareness of such deaths post-COVID vaccine which likely assisted in his ability to be aware of the deaths.

But the core statistical analysis doesn’t depend on there being a change in the “situational awareness” in this case. Only one analysis noted above is dependent in any way on this (specifically noted in the analysis).

The bottom line is that the truth is likely much worse than Jay is aware of.

Can Jay really know that many people?

Not really. I have 8,377 people in my contacts. These are all people I’ve met and spoken with because all my contact are manually added. So if I reported 100 people died suddenly who were people I know previously, we can calculate a ratio.

In Jay’s case, adding up all his contacts gives us an estimate of the size of his friend network. Everyone who died was in that friend network. Undoubtedly, there were people who died who are in Jay’s contacts who is does not know about yet. So the numbers in this article are a “best case” estimate in terms of giving the vaccine the benefit of the doubt; the overall % of Jay’s verifiable friends who died is likely much larger than described here because of the number of deaths he isn’t aware of yet.

In short, if we believe CaptainMeatBall, it’s much worse than killing 1 per 1,000 doses.

Comments on Fenton’s YouTube video

All my comments on YouTube, for any video on any topic, are all automatically deleted within 30 seconds after I make them, regardless of what I wrote.

Are you surprised?

The key points are:

1. No, the data wasn’t made up. It’s all verifiable.
2. If you have a counter anecdote that proves the vaccines are safe, you are welcome to post it. I haven’t seen it. It would have to show someone where the number of unvaccinated who died suddenly is the the same proportion as the number of vaccinated who died suddenly. I’ve just never heard of any such anecdote.
3. Even if I cherry picked the data from 81M interviews of people, it doesn’t change the fact that the anecdote still proves conclusively that the vaccine is killing people at a rate that is orders of magnitudes higher than the FDA/CDC claims.

Fact checker notice

It’s important to get the truth out to people.

Summary

1. The most important conclusion, and that is unassailable, is that the FDA/CDC are lying about the claimed safety level of the vaccine. They’ve told us that the vaccines are “safe” which means, effectively, kills fewer than 1 person per million doses. There is simply no way this could be true because Jay’s case, which is a lower bound on the kill rate, would be statistically impossible to ever come across. The vaccines are not even close to that level of safety. They are lying.
2. Jay’s anecdote is statistically very unlikely to happen by random chance. It’s a near certainty that the vaccine is killing people at a rate that is higher than the unvaccinated are dying over the same period of time.
3. My best point estimate of the rate the vaccine is killing people is 1 death per 1,000 doses. This has been noted earlier and confirmed, yet again, by this anecdote.
4. Jay’s data also shows it is highly unlikely that the the vaccine could be killing fewer than 1 person per 10,000 doses. In other words, it’s a safe bet that the vaccines are unsafe.
5. Jay’s data does not preclude the possibility that the death rate could be higher than 1 per 1,000, i.e., that the vaccine is more deadly than I think. There were likely deaths among Jay’s friends he has not yet learned about.
6. We gave a liberal estimate of Jay’s friends. Some people claim that nobody knows this many people. For those with that belief, the statistics calculated here are thus a conservative estimate and the vaccine is WORSE than is portrayed here.
7. The obituaries of all the people who died on the same day that they were COVID vaccinated never point out that the temporal association with the vaccine. Apparently, people believe that hiding that data from public view so that people don’t know the truth is going to save lives. I find that baffling.

Finally, YouTube censors comments that go against the narrative. I tried to respond to commenters on Professor Fenton’s YouTube video by citing the source of the data, but every single one of my comments was removed by YouTube in less than 30 seconds. It was a complete waste of my time.

We are left with the conclusion that we are being lied to and that the vaccines are not safe.

Jay can’t be credibly accused of making this story up. The names of those who died are revealed and how he knows them. Jay will assist any fact checker with verification of vaccination status for those who died.

All the deaths he cited were people he knew. And all died unexpectedly.

The statistics for just this one anecdote are consistent with the statistics I’ve found in earlier investigations: the COVID vaccines are killing 1 person per 1,000 doses on average (causing 675,000 deaths in the US).

Jay’s story confirms what we’ve known for a long time: the CDC is lying to people. The COVID vaccines are unsafe. Nobody should take them.

The reality simply doesn’t match the rhetoric. When that happens, reality should always win.

Yet in our society, the medical community prioritizes rhetoric ahead of reality. That’s a big problem.