During my review of the weekly VAERS data, I discovered the data reports have decreased for specific states and categories. Typically, every two weeks I review the OpenVAERS COVID-19 data summaries and update my tables. Assuming there has not been an error by OpenVAERS, For example, I noted the number of VAERS reports for Florida decreased, specifically for the 65-80 age range. Between January 28, 2022 and February 11, 2022 the number of reports for the 65-80 age group declined from 11,883 to 11,734. It should also be noted in my review of the overall child COVID-19 vaccine data I found several categories showed a decrease in a specific "injury" area, such as Myocarditis, and for overall state reports. I am unsure if the problem resulted from an erroneous data analysis by OpenVAERS or if in fact, the CDC has removed reports from their site.
I realize the VAERS site is significantly underreported, but this is published data from the institution that guides medical decision making and practices.
I totally agree--this vaccine causes injury.
A variable unique to the COVID-19 Pfizer and Moderna vaccines is the lipid nanoparticles/mRNA technology. Also, different individuals in the control group receive different doses and batches. Other components have also been found in the COVID-19 vials such as graphene and unidentified organisms and structures. I believe the intent of this vaccine is known by a small percentage of the world's population, but we can all agree the people are part of an experiment (it's just that many do not realize it).
People are corruptible and so, too, is the science.
Again, you're making an assumption based on evidence I don't believe you have.
Pfizer and Moderna does indeed list known issues.
That does NOT mean that every issue of this type in VAERS is legitimate. It doesn't even mean that 99% of the issues in VAERS are legitimate. I'm not suggesting that these are fraudulent reports. Just ones that were submitted (because VAERS wanted them) that are not actually vaccine injuries.
A study of 2.5 million vaccinated individuals found myocarditis in 54 of them.
Based on this study, I can extrapolate that there is around a .00216% chance of myocarditis arising from vaccination, and of those, 76% will be considered mild and make a full recovery quickly.
https://www.nejm.org/doi/full/10.1056/NEJMoa2110737
I know, I know, the communist scientists are lying and everything. And this is only one study. But the communist scientists are also the ones who that VAERS database is designed for, not you.
You are correct that there are real vaccine injuries. You are correct that with a large enough number of vaccines in a database, some of them will probably show vaccine injuries.
You are unable to demonstrate what percentage of those reports are legitimate using any empirical measure. You specifically said "you assume" that only 5% is worthless.
If you can show me mathematically how you arrive at a conclusion that 95% of VAERS reports are considered actual vaccine injuries based on the data it presents, you will have made your point.
But I want to see something more than, "Just trust me, I know data stuff and use AI." Because you aren't Q, and you don't get the benefit of the doubt from the audience here. :)
You're getting confused between the usefulness of aggregate in establishing a stable mean output (which is true) and the usefulness of aggregate in supporting unverified postulates. The latter is a fallacy called "bandwagon appeal": there are lots of people who say this thing is true, without proving it, and therefore, it must be true.
I'm going to open a website that collects reports of dangerous interactions with Q people. I will not verify anything. But police MUST respond by uploading ALL police encounters with suspected Q people, just in case they're dangerous. Also, any random citizen is also allowed to upload any encounters they believe they had with crazy Q people.
My database has 1,000,000 reports by next month of psychotic Q people causing mischief and violence.
Based on your argument, I can then confidently state that there is an epidemic of Q-related violence in this country, because my Q-violence tip line website has a lot of reports, and according to the law of big numbers, that must mean that a significant number of those reports must be based on real life.
I'm just going to assume that only 5% of these 1,000,000 reports are worthless. That's a pretty big problem.
Except, of course, that I haven't proven that ANY Q people are capable of violence. And even if it seems logical that there must be SOME unhinged Q people out there, is there a basis for me to assume that even the vast majority of my tip line website is based off of reality? Does the fact that I have a lot of unverified reports prove that these reports reflect reality?
Or is it just the bandwagon appeal fallacy?
In this case, I have an enormous aggregate of reports suggesting Q people are violently crazy. BUT, because I am SPECIFICALLY ASKING FOR THOSE STORIES, and because my website VERIFIES NOTHING, then my website's aggregate conclusion can't be empirically supported, because I have no empirical evidence that even a single Q person was verified to have committed violence via my website. Just lots of unverified reports.
Exactly the same with VAERS.
I am not trying to pre-determine data usefulness. The way in which the data is useful is explicitly stated by VAERS.
Let me boil down our conversation hencefar.
"Let's assume 0.1 percent of the reports are valid."
"Why?"
"Because the vaccine must be doing SOME sort of damage."
"But how do you know how vaccine damage is represented in VAERS if it doesn't verify its data?"
"Because I can assume if there are a lot of reports, there must be some representation of vaccine damage. Law of big numbers."
"Yes, but how do you know HOW MUCH vaccine damage is actually occurring within those VAERS reports if the VAERS reports don't verify that ANY vaccine damage has actually occurred?"
"Because there are a lot of reports, and the vaccine is doing damage, so some of these must be valid."
"But how do you know which of these reports represents damage? How are you assuming that the number of vaccine damage cases is even statistically significant? How can you assume what proportion of the cases are valid based on this data?"
"I know data and AI. I accept no further demands for explaining my assumptions."
I have a feeling that at this point, you are well aware that the mere ability to say that an outlier exists in some large population is NOT the same thing as saying the outlier exists in some specific proportion to the population. That is where your math fails, because if you cannot verify some proportion of your cases, then any extrapolation you make is implied, not empirical.
Do they list how often these known issues occur in the vaccinated population? Because that is where you are making assumptions.