Why the Corona "vaccine" trials were useless

Another case where you have to know your statistics, and where I present some content I have not seen anywhere else.

As of now we’ve known for about one and half years that the Corona “vaccines” did not come close to the sales pitch for them. “Get the jab and you won’t get Corona.” was the first one to go, and it started to crumble in June of 2021. The claim of how you getting “vaccinated” will stop the spread and protect others quickly followed, and if you were actually reading true news sources you had no doubts by late July 2021 that the “vaccines” did NOT stop infection and they did NOT stop spread. The so called “experts” in government agencies started admitting this in August of 2021, but they insisted that the “vaccines” still prevented serious illness and death.

I’m not going to include a bunch of links on that past history from 2021 here, because I already did in a previous post. Check that post out if you have any doubts about the timing of the changes to the story in 2021, and for a bonus there was data even then that preventing serious illness and death was also false:

The last "vaccine" lie standing takes a huge hit.

Recently more new data has come out showing that the “vaccinated” are far worse off than the unvaccinated who had prior infections. In brief, it’s mega confirmation that natural immunity is far better than these experimental gene therapy clot inducing and heart damaging shots. Covering that is not the main point of this post.

My point here is to show that even if Pfizer’s trials were run 100% correctly, on a fully representative group of test subjects, they wouldn’t have told you much of anything efficacy wise. By “correctly” I mean according to the procedure they claimed was used. I’m not saying that their procedure is correct because it’s not, but there were serious issues raised in how the trial was conducted, including claims of falsified data. Without even getting into that, I can show that the trials were useless.

To understand that you need some background on how dangerous Corona really is, again from truthful sources. In another previous post I talked about “co-morbidities” (health problems that were pre-existing in people who supposedly died of Corona) and a prominent expert’s estimate of the death rate from Corona. If you don’t want to follow the link below to that post, I’ll tell you that per the CDC, only 5% of the claimed deaths were in healthy people, and per John Ionnadis the OVERALL death rate from Corona is only about 0.15%.

Per the CDC's own data, 95% of deaths listed as due to Corona had other serious health conditions.

I stress OVERALL, because that includes all the deaths of the elderly and those in horrible health. Assuming all the listed Corona deaths are truly Corona deaths (and they’re not, not by miles), whatever the overall death rate is you need to divide by 20 to get the death rate for healthy people. (If only 5% of healthy people are the ones dying, that’s 1/20th of the total, thus you divide by 20.)

Using the overall death rate number of 0.15%, we need to look at the survival rate for a handy comparison. So 100% - 0.15% means that 99.85% of all people who get infected with Corona will survive Corona, again per John Ionnadis. For the sake of argument, let’s say Ionnadis is way off and the survival rate is only 99.5% instead of 99.85%. Yes that is way off because that means over 3 times as many people who got Corona have died from Corona than what Ionnadis says. This also allows for other estimates where the death rate is claimed to be up to 0.5%. (I’m not going to indulge absurd fear porn claims that go up to and over a 1% death rate, but even that high my points below do not change.)

So if we look at this from a product testing standpoint, such as what happens to the parts inside your cell phone on the production test floor at a factory, we’ve got a yield of 99.5%, and we are trying to improve it with a change. In order to fully confirm we’ve gone from 99.5% to 99.7%, for example, do you think testing 50,000 parts is enough? What if half of the test parts don’t get the upgrade or change? What if there are other variables included, like there are differences between people? I can tell you right now that no factory would claim with certainty to have made an improvement so small to such a high yield with so few parts. Going from 99.5% to 99.7% is far different than going from 60% to 70%. You need MILLIONS of test parts, and you need MILLIONS of people for testing drugs/vaccines to determine their efficacy when the survival rate is already so high.

What about healthy people and how do they affect what number of test subjects is needed? Taking Ionnadis’s overall number and dividing by 20, you can see that for the healthy people of all ages the survival rate is 99.9925%!!! How are you going to test to see if you’re improving that outcome?? And what if the majority of your pool of test subjects actually excludes most of the people at the greatest risk, very old people and people with 3 or more co-morbidities, like Pfizer’s largest study groups did? (I’ve seen the data but don’t have it at hand, so I’ll add it as a comment to this thread like I add additional info in comments on so many other posts here.)

So Pfizer’s got a group of people that has very few of those at the greatest risk, and that’s not all. Pfizer has no way of knowing for sure how many of those people have pre-existing immunity. Per one of Pfizer’s own former chief scientists, Michael Yeadon, an infection with the SARS 1 virus back in the 2003 means you have immunity to SARS 2, AKA Corona, AKA the term I never use, “COVID-19”. Yeadon says other Coronavirus exposure also builds immunity to this new Corona.

In addition, Pfizer’s total of test subjects was apparently 43,000, and of course about half of those got saline solution for the trial. On top of all the other problems with their data, they essentially “deleted” their placebo group by giving them the experimental gene therapy shortly after the end of the trial. So of course Pfizer has no long term safety data, and in fact don’t even have medium term safety data. They started their trials in May of 2020 and I believe they were done by late October 2020. So not really even 6 months of safety data, and certainly not even close to one year. Given the highly experimental nature of the jabs, it’s bizarre that this was even allowed.

So Pfizer’s total test group size is much too small, it’s not representative of the population, and Pfizer has no way of knowing how many of the people in their test groups have pre-existing immunity. You know people have pre-existing immunity, or they have super great immune systems otherwise because they get exposed to Corona and they get very mild symptoms or even no symptoms at all.

Based on numerous data sources since the scamdemic/plandemic began, at least 50% of the population fits into this category of having prior immunity somehow. So jabbing this 50% or more in your study is NOT going to tell you anything. They’re not going to get sick in either group, so now your effective group size just got much smaller.

But it gets worse than that. In my production test example of cell phone parts above, there is a crucial difference. The part under test is actually exposed to the test conditions right then and there. There is no guessing as to whether it passed those conditions at some point in the future. If you’re testing a “vaccine” for an illness, even if it’s a real vaccine and no matter what the illness is, your test subjects do NOT get directly exposed to the virus as part of your testing!

For a test of an airborne virus vaccine, to really know if it worked you would have to have infected people coughing in the faces of both the vaccine group and the control/placebo group, and then you would see how many got sick. Of course Pfizer didn’t do that, so now you have to add in the extra guesswork as to whether any given test subject was actually exposed to Corona.

The net result is an overwhelming set of errors such that Pfizer’s tests don’t tell you anything other than that they trigger antibody production. And of course you should know by now that the antibodies produced appear to be very “variant” specific, and they appear to be very short lived. So even if you have some partial protection (and I still question even this), it’s not going to last more than a few months. And as of now jabbed people are MUCH worse off than unjabbed people. Per a new study the jabbed are 3 times more likely to get infected, and 4 times more likely to become severely ill or die.

So why in the hell are the Corona vaccines still being used at all, let alone still being forced on people in certain cases??? Because of billions in profits to big pharma, and many millions in kickbacks to everyone pushing the jabs.

Why wasn’t there extensive messaging from the CDC, the NIH, little fascist Fauci and company, very early on telling people to lose weight like their life depended on it, get healthy in any other way they can, exercise, take vitamin D, take zinc, etc.??? Because fewer people getting sick and dying means way less fear, and ultimately WAY less money for big pharma, and so way less in kickbacks for all the people helping to stoke the fear at all levels.

If you doubt anything I’ve said here, try explaining why all of the relevant “officials” were insisting in early 2021 that the “vaccines” would prevent you from getting sick, and would prevent you from spreading Corona, and would mean everything would go back to normal. Pfizer hadn’t even tested whether their “vaccine” could stop spread, so why make the claim other than in a blind drive for profits??

While you’re at it, explain why Pfizer had more deaths in the “vaccine” group than they did in the placebo group. Finally, explain why Pfizer tried to hide it’s trial data for 75 years. Don’t tell me how it was really the FDA trying to hide the data, or it was an issue of how long it would take to release it, or another ridiculous excuses. You don’t try to hide data for decades if that data supports your claims, you want it out there ASAP for all the world to see.