A short seminar for anyone still denying the level of morbidity and mortality induced by the covid shots
Numbers don't lie, and when the truths they tell are both resounding and devastating, even professional data-distorters can only hide from them
How is it possible to know if a change in the frequency of a given event represents a true statistical outlier? That is, something so unlikely to occur based on accumulated human experience that it almost had to be caused by something newly introduced into the analytical environment, but unaccounted for and perhaps undetected?
Say three kids in the same 1,000-student school all develop the same non-communicable illness in the same academic year when the same illness used to strike one student every five years. Is that just a burp likely to happen “occasionally” or is it such a deviation from the norm—maybe even a black swan event—that an observer can be virtually certain that a new variable has entered the system?
Your intuition here is somewhere between worthless and useless. So is mine. That’s why I reject it. And intuition is among the many weak spots in the normie class professional data-manipulators exploit to deceive you, using the mass media.
In order to formally classify something as “unlikely,” you need a firm idea of what is formally “likely.” For that, you need data. For example, say you’re the creator of an online newsletter and have noticed for years that the number e-mail subscribers you have at any point—with this number growing linearly with time—correlates strongly with the amount of traffic the Web version of your newsletter receives. For example, when your newsletter had 5,000 e-mail subscribers, your website was getting around 15,000 distinct visitors a day, with a modest day-to-day variation; when it reached 10,000 subscribers, the Internet presence was drawing around 30,000 unique visitors a day, with around the same delta. (This kind of reliable and predictable relationship might make sense if you never share your content on social-media sites and no one else does either, which is not how most modern websites work.)
Let’s say that, when you were at the 5,000-subscriber level, for about a week straight, the website version of the newsletter collected about 30,000 to 40,000 visitors per day. Absent other information, you could conclude from this—if you wanted to—that your writing had suddenly become so interesting that the majority of your regular readers from all sources were suddenly sending a couple of friends links to your most recent posts, and that that spate of suggestions had resulted in your site receiving twice as many regular Internet visitors as before.
But if your daily website traffic fell the next week to its recent baseline level of around 15,000 daily visitors, you might be prepared to face the fairly obvious: Someone must have posted a link to a single one of your posts on a high-traffic site, and this alone had generated the increased traffic, all of which proved to be temporary. (Assume you cannot detect where your visitors are coming from, browser-wise.)
Problems like these—“How likely is it that this unusual pattern is purely the result of chance, that is, happened without any new variables capable of influencing the result being added to the mix?”—can be modeled using the Poisson distribution. The above example actually isn’t the best real-world candidate for such modeling, although it works okay if enough clumsy assumptions are baked into the parameters of the problem to force numbers that aren’t actually constant to behave as if constant.
Real Statistics Using Excel has a number of real-world examples of the application of Poisson distributions, which are used to determine the probability that a certain number of discrete events occur during a fixed time interval when the events occur independently and at a constant average rate.
Now let’s say you have a country with a population of around 335 million, with births, deaths, and other events affecting the nation’s populations occurring at predictable-enough rates, and with pronounced season-to-season variation (at least for the deaths) within years. “Expected deaths” is not a precise number and never could be, which is why people who don’t like to own up to the damage done by the covid shots emphasize its imprecise-ness: They do this in an effort to disqualify the undeniable and very useful value of expected deaths altogether. These people are usually scurrilous, while others simply have no clue what they’re talking about.
I had to learn this kind of math as a physics major. I recall using it only once outside of solving textbook problems, when I had to perform a lab experiment that involved electrons striking a photographic plate thanks to a process called beta-decay (“beta particles” are just electrons). I think the atoms used were phosphorus, because my advisor, who had a Ph.D. from Carnegie Mellon University, asked us not to eat the sample. Maybe it was actually plutonium. No, it was probably phosphorus, but neither I nor my lab partner Dan, despite being old-school, either ate from or ignited the sample.
The idea was to determine the frequency of emitted beta-particles striking the plate over a period of around two days, and see if this conformed to results published in the literature. I don’t remember the results, and I doubt I consulted the literature, but the formula for the Poisson distribution is wonderfully simple1:
Here, e is Euler’s number, a constant equal to about 2.71828 that is important in problems involving natural logarithms, such as determining the amount of a monthly mortgage payments, or pinpointing someone’s time of death. λ (lambda) is the established mean number of events per unit time, or the expected number. x is the observed number of events per unit time—the number suspected of being strange. x! is “x-factorial,” the number given by multiplying x by (x-1), then multiplying the result by (x-2), all the way down to 1. Thus 5! = (5)(4)(3)(2)(1) = 120. (Factorials come in handy when determining how dismal your odds are of winning the Powerball. Both λ and x must be a whole number, which makes sense when talking about discrete events like calls to a call-center or deaths.
Example: Say you normally receive 10 complaints a week about the content of your newsletter. What is the probability of getting 15 complaints, with no changes to this “system”?
Here λ = 10 and x = 15. Although I claimed that the above equation is simple, that doesn’t mean anyone should work with it using pen and paper. Instead, use an online tool like StatTrek’s, and you quickly come up with these figures:
So, there’s a 3.5 percent chance of getting exactly 15 complaints and a 4.9 percent chance of getting even more than that, or an 8.3 percent chance of getting at least 15 complaints. The chances of receiving fewer than 15 complaints is therefore 91.7 percent.
So, you probably just had a fun week; nothing here to really suggest, say, that either you became bristlier or others grew more sensitive.
A man named Steve Kirsch has been using techniques like these to investigate the level of effectiveness and (especially) the level of safety of the covid shots. Kirsch graduated from the Massachusetts Institute of Technology in 1980 with degrees in electrical engineering and computer science and make a lot of money in tech. After believing himself to have been injured by the shots, he poured all of his attention into exposing the dangers of the mRNA jabs via his Substack.
Since Kirsch is rich—and whether anyone likes it or not, it takes a rich guy determined to be a chronic thumb in the eye of the powers that be to even gain any traction with such a project; see also Edward Dowd—he has been offering to bet experts who disagree with his conclusions large sums of money to prove him wrong. Instead of doing this, these experts and the media have smeared him in the usual ways—ruining his Wikipedia page (Glenn Greenwald recently dedicated an entire System Update episode to the takeover of that platform by the global establishment, mostly through the efforts of the Central Intelligence Agency) and describing him as an misinformation-superspreader, a preferred term among the people who are themselves the world’s most egregious and malevolent liars.
But none of them will take him up on any of his bets, or even engage with his math. It’s just, “Aw, that guy, playing doctor again,” with no attempt at all at the material smackdowns in the form of refuting Kirsch’s computations—traditionally a requirement when condescendingly calling someone else an idiot.
Kirsch probably has more raw intelligence that at least 90 percent of practicing physicians and he’s definitely better at math than at least 99.8 percent of them. But the point here is that there really is no way for even the smartest person in the world to invalidate Kirsch’s claims. And he’s made a dizzying pile of them, all backed up using time-tested, gold-standard statistical techniques. You may want to start with this one, as it both illustrates the kind of tedious but invaluable math needed to expose the CDC, the FDA, Anthony Fauci, Bill Gates (and the World Health Organization, Gates in effect co-owns along with China) and many, many others as liars and reveals another horrifying truth about the shots.
A competent statistician—or, say, someone with a musty physics degree who has taught high-school math and tutored college students in statistics—could debunk any of Kirsch’s calculations and disprove his claims in minutes, and make up to a million dollars in the process. Either Kirsch’s datasets are no good (because he has made flawed assumptions about the data) or his calculations are off (which anyone can confirm is not the case). Yet none of the people he contacts will engage him.
Anyone who seriously believes at this point that this is because cranks simply aren’t worth the time of a knowledgeable expert—even from alleged scientific experts whose careers revolve entirely around making claims about C-19 and the shots—has a serious problem with their reasoning. And I don’t think anyone who lands on this conclusion is using any kind of reasoning process anyway. I think people are simply really afraid—not just of the potential or ascertained damage done to them by the covid jabs in particular, but of confronting how many awful lies we’ve all been laboring under without really thinking about them for many years.
In addition to not engaging with Kirsch or anyone like him (see also: Robert F. Kennedy, Jr., Pierre Kory, Bret Weinstein, Robert Malone, Jay Bhattacharya…), the experts” have another problem: No alternative explanation whatsoever for the massive increase in deaths in jabbed-up countries. The few mainstream-media stories that mention an uptick in deaths will quote experts blaming the phenomenon on climate change or even covid, but they won’t even mention the shots—ever. These outlets all know people are deeply curious, and that even people who believe in the good of the shots would love to see one of these stories say, “And if you think it was the covid vaccines, here’s why that’s untrue.” They go nowhere near it and are just dug right the hell in until it’s all over—either until they die and exposure of their misdeeds no longer matters to them, or the rest of us stick to our side of the one-sided agreement and die first.
Now, published research is a completely different ballgame. For one thing, anyone wanting to “prove” a desired but sham conclusion can do so using valid statistical methods by properly gaming the study parameters and intentionally bitchrigging the statistical analysis. This has traditionally been done by generating a “statistically significant” but weak result by either using too small a number of subjects or essentially requiring nothing of the intervention under scrutiny. This is exactly how the makers of Vivitrol, a naloxone injection I once allowed myself to get falsely advertised to control cravings, got their net-harm-inducing and expensive product to market. It’s one way to obtain a P-value of < 0.05 (the usual threshold for establishing “statistical significance”) while adding nothing of value at all because the study is low in power—that is, beset with not only Type I errors, or false positive results (meaning that too permissive a P-value was selected) but Type II errors, or false negative results.
Concerning covid research specifically, a major issue from the lying “safe and effective” types has been using blatantly substandard datasets. The next time you see a graph some pro-shots goon or sincere but hapless apologist had produced that “proves” the shots are safe, consider the amount of statistical noise involved, even in the few of these efforts in which people are wrong in earnest. It makes things hard enough that any group of study subjects can be divided into multiple binary categories: jabbed or unjabbed? With which type of “vaccine”? Had covid or didn’t have covid? And in what order?
Recall also that the CDC considers people unvaccinated if they only had one mRNA shot in the original two-shot series—in fact, until at least a week has passed since they got second one. So if you get covid, die, or both right after getting the first jab, your corpse gets lumped in with the "“antivaxxers.”
Of course, it’s also possible for shots-haters hoping to convince people they shots are unsafe to bitch-rig studies or present sham interpretations of what was actually in them—the equivalent of a lazy cop planting evidence on someone the entire world knows is guilty and therefore threatening to ruin the legal case against the guy, even when there’s video of him doing whatever he’s accused of. Alternatively, sometimes these people are merely wrong about what they see even when their general conclusion that the shots are unsafe is spot on.
I have vetted the studies I have linked to here in the sense that people like Kory and Bhattacharya have validated them. But “luckily,” none of that is even necessary when you have Kirsch’s data. A single genuine black swan event is sufficient to prove that the shots are unsafe. And it’s not like the analyses he’s doing conflict with what people are seeing all around them; the media are admitting to a rapid pile-up of younger American corpses because they can’t bury this information, so they blame extremely high mortality rates on things like climate change and hope this somehow continues to fool people.
Dowd is a former BlackRock asset manager and the author of Cause Unknown who has been crunching covid-related data instead of Wall Street numbers over the past few years. His company Phinance Technologies is devoted largely to rooting out trends in injuries, disabilities, and deaths induced by the covid shots—that is, the human cost. Dowd has posted details about the methodologies his team relies on to estimate excess covid-shots deaths. Anyone who wants to argue with his dire numbers (and so far, no one has) needs to engage the methodologies; as anyone can see that these are sound, the radio-silence from jabs-advocates will continue, and people will continue to die as a result of the jabs.
I’ve previously mentioned Vinay Prasad, an M.D. who began positioning himself as a “brave” critic of the shots as soon as it was evident they were causing a big uptick in myocarditis cases, especially but not only in younger males. Prasad, it turns out, is either dumb or dishonest. And since he’s not dumb, he’s being dishonest either because he is a coward or because he’s on the take.
Two of his recent posts demonstrate the game he's playing. (If you’re aware that Prasad is also a retard concerning the efficacy of ivermectin in treating covid infections, forget that for now.) Prasad keeps pretending to bash misinformation-spreaders while actually doing nothing but pushing the idea the shots are a good idea in general when he almost has to know better.
Here, Prasad claims:
Myocarditis does outweigh the benefits of vaccinations for some ages, in men, and some doses
The risks outweigh the benefits in everyone. This is easy to show, especially because the jabs are not done killing people. It's just math. That's why everyone ignores Steve Kirsch.
And here, Prasad writes:
COVID vaccines should have been promoted in the elderly, non-immune, but weren’t sufficiently
There is zero evidence for this and plenty against it. And Prasad also won’t engage with the number of excess deaths induced by “something” in the U.S. starting in the spring of 2021. Even if somehow continues ignoring the number of people dying and what they're dying from, he understands just like the rest of us that these are experimental shots that humans have only been receiving at scale for around two years. The “controlled experiment” is underway in the form of 230 million or so jabbed persons in the United States alone.
I’ve had to update the way I view the world, and especially medicine, as a result of the covid-panic that, in a saner world, would instead be mass-jabs-panic. (Obviously, a sane society would never feature being getting intentionally poisoned while berating far wiser people for not doing so.) Not only are the covid jabs incredibly dangerous, but childhood vaccines long considered safe are anything but—Kirsch and others have shown that there is a clear connection between their administration and not only autism and other forms of childhood-onset neuropsychiatric mayhem but sudden infant death syndrome (SIDS).
The good news is that, according to Dowd, about 82 percent of jabbed people are actually unharmed so far, to the extent this can be known; these people should be getting routine cancer screenings.
This sucks, for sure. But I don’t mean to attack anyone who got these shots as if they were fools. I got one non-mRNA-based product that itself is more redolent of the stench of dead bodies than of the scent of a rose garden, so I empathize. I don’t have a lot of close friends and the ones I have are, as a rule, as “vaccinated” as the average American. Some are plainly ill from the shots, with the second one in the original series usually associated with the highest frequency and levels of immediate ugliness
But it’s time for people to stop dillydicking around and own up to what’s happening. Anyone who can find a way to show that Steve Kirsch is wrong has to do this by attacking his work, not rely on his laughably slanderous Wikipedia page or mainstream-media slurs about him to dismiss him as a crank. Any dire claims I post here about the “vaccines” are just as statistically sound as Kirsch’s, which is probably why any adversarial responses to these consist of either people no longer reading what I write or people who rely on ad hominem responses like “Don’t you think your anger about Wokism is causing you to over-catastrophize?” Actually, people going full retard in response to the catastrophic numbers I post and pretending they’re inaccurate is what pissed me off.
And this is why I really, really despise people who do know better and lock out those who sound the danger alarm, like the five members of the Boulder County Board of Health I’ve been yacking about plus a couple of others I learned yesterday are part of the same disinformation-racket and garden-variety grifting.
Finally, I hadn’t thought about this in a while, but perhaps the only formal legacy of my spending any time in medical school is a study published in 1996 in American Journal of Respiratory and Critical Care Medicine. I did most of the raw data collection for this project (it wasn't difficult).
This doesn’t add weight to anything I’ve written here, but it does distinguish me from resentment-driven lunatics like Aysha Mirza, who despite being in her mid-forties and obviously having neither a brain nor any relevant schooling (she doesn’t have a master’s degree) insist she’s bound for medical school eventually. She is just smart enough to grasp that she was always too stupid for this to be possible (and you don’t have to be very smart to be admitted to medical school) and spends her time bashing people who actually did get admitted to med school. Her maniacal quest to mask and jab up the world is rooted in nothing more than a worsening hatred of her own insufficiencies as well as of the insecurity that compels her to lie nonstop. (Go ahead and ask her for evidence of her credentials—it’ll be a funny thirty seconds before she blocks you.)
Mirza can’t help being a self-loathing nutjob, but she could be gently encouraged to abandon her Twitter/X fraudulence by those still active on the platform; even though her audience is tiny, she’s still giving dangerous advice. And at the rate she’s going with claimed “boosters,” she’ll wind up a less racist but equally strange-looking version of this unfortunate brainwashing victim.
[3:28 p.m. UPDATE: Minutes after I posted this, the following comment appeared:
"After believing himself to have been injured by the shots, he poured all of his attention into exposing the dangers of the mRNA jabs via his Substack." So yeah, with motivated reasoning there is about a 0% chance this guy would find disconfirming evidence.”
I typed up a response, but when I tried to post it I discovered that the comment had already been deleted by its author—maybe after they re-read, or reached, the part of my post reminding people to attack the math, not the person doing it, unless they want to sound like a standard unhelpful hammerhead.
I’ll respond anyway: This is or briefly was) a perfect example of someone using all the reasoning power of a Biblical creationist. First, what exactly is the proper course of action for people who believe they’ve been injured by the shots—shut the up and accept it? No fellating the state for me, thanks. And second, if any of the math Kirsch has embarked on owing to his suspicious is wrong, someone can surely demonstrate this.
It’s not clear to me why anyone would even argue on behalf of the safety of these shots at this stage unless they’re concerned about negative consequences to themselves or their relatives. Scrambling to look for ways to discredit what I post about the shots is, at this point, an otherwise inexplicable scramble to look like an opponent of math-based logic itself.]
It may not look like a simple equation. A better way to put it is that its solutions are easily obtained. An example of a famous equation is the Schrödinger equation, forming the conceptual cornerstone of quantum mechanics, it often expressed in the form below:
where the upside-down triangle is a Laplacian operator, h-bar is a form Planck’s constant and concerns a particle’s angular momentum, m is mass, E is total energy, V is potential energy, and the psi (ψ) is a wavefunction.
Everyone has heard of this equation, or at least the corresponding boxed-up cat, but it is basically impossible to work with without using computers beyond the simple example of an infinite square well.