For those that haven’t seen, a danish study showed that vaccine effectiveness was NEGATIVE for omicron variant after 3 months post vax, meaning that the vaccinated are more likely to catch covid than the unvaxxed. That is because the ADE effect kicks in as antibodies drop AND antibody affinity for the new variant drops.
Before, they were saying that immunity should last a year, then it was 8 months, now we are down to three. Risk your life with a dangerous vaccine which will increase your chance of covid for the first 2 weeks and will increase your susceptibility to other infectious diseases, so that it will work 3 lousy months, and then you’ll be at greater risk for covid than if you had done nothing. What a joke.
And if 80% are vaxed and 'covid cases' are rising as they keep saying then statistically speaking the ones spreading covid and making the cases rise have to be the vaxed as they are in the big majority of the entire population. A person would be way more likely to encounter a vaxed person than a unvaxed person.
It's just baffling to me how people can still play along with this obvious charade...
The abuse of statistics these past two years is one of the more offensive parts of the entire op. Like most globohomo ops it depends on exploiting the limits of average intelligence. They've been using Bad Stats 101 the entire time.
First, set arbitrary parameters. "Vaccinated" vs "Unvaccinated." For the purposes of a comparison, it is good to use "on/off" level critera, ie a fundamental difference between the two things being compared that hinges upon what you seek to actually compare. Unfortunately this does not satisfy that as "unvaccinated" is a fundamental status but "vaccinated" actually is not. No matter what trickery you want to claim, the sheer fact that vaccinated ALWAYS has multiple contingencies to meet the criteria makes it an unsatisfactory comparison criteria. It's literally comparing apples and oranges when you are suppposed to be comparing apples and not apples.
The reality is that you can't properly measure vaccinated/unvaccinated in the middle of an outbreak. They know this, it's a basic tenet of vaccination. The best you can do is to draw a hard line criteria that defines it without ambiguity, and ONLY include data that meets that criteria. For example, define "Vaccinated" as someone who has received two shots at the prescribed interval and who was tested to confirm no presence of the illness in between. Only a person who has gotten two shots without being exposed to the disease, and is now 14 days beyond receiving the second shot can be counted for statistical purposes as "vaccinated" when comparing to unvaccinated. Any individual who did not meet that criteria is removed from the data set.
Next, because of the nature of vaccination you also need to keep track of people during the vaccination "cycle" to see if they're getting ill. That is why you test, confirm negative, give a shot, test, confirm negative, give second shot, and test again before 14 days are past. YOU THEN REPORT THE RESULTS OF THAT. This data tells you things ONLY about people who are vaccinated, and helps you isolate the effects of the vaccination, ie does it give you the disease or some other malady.
For additional comparison, you need to ensure you have a sound basis for comparison between your data sets. This means that the people who you count as "unvaccinated" and "vaccinated" according to the above criteria must also be balanced according to age, weight, cofactors, and general health. A population-wide comparison is mostly useless because of course these factors change how one responds with and without the vaccinations. To get a clear picture of the difference you take two equal groups, usually ones with the least complication, such as 30-50 year olds of healthy weight and no known cofactors, and compare the outcomes for ONLY that group using the unvaxxed/vaxed criteria. You do the same for older, fatter, less healthy, again comparing against each other.
This is basic statistical analysis anyone who took two classes in it would know to do.
Its my pet peeve. Really its hard to know exactly how they game the stats unless you have access to all the raw data. When you really know the data set you can produce some spectacularly misleading stats by “scrubbing data” and finding various justifications for omitting data or generating stats in unintuitive ways.
It seems obvious at this point that the vaccine is least effective on the elderly who were the only people that really needed it to work. So they roll up 80 year olds with 30 year olds. It seems clear that the vaccine get less and less effective over time so Im guessing they are rolling up a lot of old effectiveness data in the the effectiveness stat. Im sure they have a bunch of tricks no one has thought of.
This woman Meryl Nass discusses the data fuckery a bit in the video here. It was abt 20 min but informative.,
This is why i don't bother to pay attention to or argue the stats. It's sufficient to show that the statistical conclusions being offered are definitely false.
More interesting to me is the more common lack of any stats to present these cases and outcomes. This reads something like this as one example of thousands: "Everyone knows that we all need to be vaccinated to stop this horrible pandemic" (blah blah blah).
Yeah so do drugs. Sometimes. In the right dose, for the right condition, in the right individual. But Im not going to start taking vincristine every week to prevent cancer. Its just shit logic.
[ + ] didyouknow
[ - ] didyouknow 6 points 2.4 yearsDec 31, 2021 15:46:58 ago (+6/-0)
[ + ] PostWallHelena
[ - ] PostWallHelena 2 points 2.4 yearsDec 31, 2021 20:28:19 ago (+2/-0)
Before, they were saying that immunity should last a year, then it was 8 months, now we are down to three. Risk your life with a dangerous vaccine which will increase your chance of covid for the first 2 weeks and will increase your susceptibility to other infectious diseases, so that it will work 3 lousy months, and then you’ll be at greater risk for covid than if you had done nothing. What a joke.
https://www.medrxiv.org/content/10.1101/2021.12.20.21267966v2.full
This is the preprint and they will undoubtedly “fix” the analysis in peer review so that it won’t look so bad.
[ + ] localsal
[ - ] localsal 2 points 2.4 yearsDec 31, 2021 15:20:50 ago (+2/-0)
The same number of vaxxed and unvaxxed people are dying.
[ + ] didyouknow
[ - ] didyouknow 3 points 2.4 yearsDec 31, 2021 15:50:16 ago (+3/-0)
It's just baffling to me how people can still play along with this obvious charade...
[ + ] SilentByAssociation
[ - ] SilentByAssociation 1 point 2.4 yearsDec 31, 2021 22:44:59 ago (+1/-0)
[ + ]deleted
[ - ] deleted 1 point 2.4 yearsDec 31, 2021 19:09:47 ago (+1/-0)
[ + ] SparklingWiggle
[ - ] SparklingWiggle 1 point 2.4 yearsDec 31, 2021 18:41:46 ago (+1/-0)
[ + ] Empire_of_the_Mind
[ - ] Empire_of_the_Mind 1 point 2.4 yearsDec 31, 2021 15:08:46 ago (+1/-0)
First, set arbitrary parameters. "Vaccinated" vs "Unvaccinated." For the purposes of a comparison, it is good to use "on/off" level critera, ie a fundamental difference between the two things being compared that hinges upon what you seek to actually compare. Unfortunately this does not satisfy that as "unvaccinated" is a fundamental status but "vaccinated" actually is not. No matter what trickery you want to claim, the sheer fact that vaccinated ALWAYS has multiple contingencies to meet the criteria makes it an unsatisfactory comparison criteria. It's literally comparing apples and oranges when you are suppposed to be comparing apples and not apples.
The reality is that you can't properly measure vaccinated/unvaccinated in the middle of an outbreak. They know this, it's a basic tenet of vaccination. The best you can do is to draw a hard line criteria that defines it without ambiguity, and ONLY include data that meets that criteria. For example, define "Vaccinated" as someone who has received two shots at the prescribed interval and who was tested to confirm no presence of the illness in between. Only a person who has gotten two shots without being exposed to the disease, and is now 14 days beyond receiving the second shot can be counted for statistical purposes as "vaccinated" when comparing to unvaccinated. Any individual who did not meet that criteria is removed from the data set.
Next, because of the nature of vaccination you also need to keep track of people during the vaccination "cycle" to see if they're getting ill. That is why you test, confirm negative, give a shot, test, confirm negative, give second shot, and test again before 14 days are past. YOU THEN REPORT THE RESULTS OF THAT. This data tells you things ONLY about people who are vaccinated, and helps you isolate the effects of the vaccination, ie does it give you the disease or some other malady.
For additional comparison, you need to ensure you have a sound basis for comparison between your data sets. This means that the people who you count as "unvaccinated" and "vaccinated" according to the above criteria must also be balanced according to age, weight, cofactors, and general health. A population-wide comparison is mostly useless because of course these factors change how one responds with and without the vaccinations. To get a clear picture of the difference you take two equal groups, usually ones with the least complication, such as 30-50 year olds of healthy weight and no known cofactors, and compare the outcomes for ONLY that group using the unvaxxed/vaxed criteria. You do the same for older, fatter, less healthy, again comparing against each other.
This is basic statistical analysis anyone who took two classes in it would know to do.
[ + ] PostWallHelena
[ - ] PostWallHelena 3 points 2.4 yearsDec 31, 2021 20:18:27 ago (+3/-0)
It seems obvious at this point that the vaccine is least effective on the elderly who were the only people that really needed it to work. So they roll up 80 year olds with 30 year olds. It seems clear that the vaccine get less and less effective over time so Im guessing they are rolling up a lot of old effectiveness data in the the effectiveness stat. Im sure they have a bunch of tricks no one has thought of.
This woman Meryl Nass discusses the data fuckery a bit in the video here. It was abt 20 min but informative.,
https://doctors4covidethics.org/replay-gold-standard-covid-science-in-practice-interdisciplinary-symposium-ii-december-10-2021/
[ + ] Empire_of_the_Mind
[ - ] Empire_of_the_Mind 1 point 2.4 yearsJan 1, 2022 01:49:26 ago (+1/-0)
[ + ] beece
[ - ] beece 2 points 2.4 yearsDec 31, 2021 18:10:42 ago (+2/-0)
[ + ] PostWallHelena
[ - ] PostWallHelena 3 points 2.4 yearsDec 31, 2021 20:02:46 ago (+3/-0)
Yeah so do drugs. Sometimes. In the right dose, for the right condition, in the right individual. But Im not going to start taking vincristine every week to prevent cancer. Its just shit logic.
[ + ] beece
[ - ] beece 0 points 2.4 yearsDec 31, 2021 18:06:03 ago (+0/-0)*