As I requested last weekend people gave me some numbers regarding how many people they knew in the following categories:
- Had a reaction to a mRNA “vaccine” which resulted in an ER visit and/or hospitalization.
- Had a reaction to a mRNA “vaccine” which resulted in long term (two or more months) adverse effects.
- Had a reaction to a mRNA “vaccine” which resulted in death.
- Had COVID-19 which which resulted in an ER visit and/or hospitalization.
- Had COVID-19 which resulted in long term (two or more months) adverse effects.
- Had COVID-19 which resulted in death.
The raw numbers and simple statistics are:
| Question |
1 |
2 |
3 |
|
4 |
5 |
6 |
|
|
Vaccine |
|
|
|
COVID-19 |
|
|
ER/Hosp | |
Long Term | |
Death | |
|
ER/Hosp | |
Long Term | |
Death |
|
1 |
0 |
0 |
|
1 |
1 |
1 |
|
0 |
0 |
0 |
|
1 |
1 |
0 |
| |
0 |
0 |
0 |
|
0 |
0 |
0 |
|
2 |
0 |
0 |
|
1 |
0 |
0 |
|
1 |
2 |
0 |
|
1 |
3 |
0 |
|
0 |
0 |
0 |
|
0 |
2 |
0 |
| |
0 |
1 |
0 |
|
1 |
0 |
2 |
|
0 |
0 |
0 |
|
0 |
0 |
1 |
|
60 |
0 |
4 |
|
1 |
2 |
0 |
|
0 |
0 |
0 |
|
0 |
0 |
1 |
|
0 |
0 |
0 |
|
0 |
1 |
0 |
|
0 |
0 |
0 |
|
0 |
0 |
0 |
|
0 |
0 |
0 |
|
1 |
0 |
0 |
|
3 |
0 |
2 |
|
1 |
0 |
0 |
|
0 |
0 |
0 |
|
0 |
1 |
2 |
|
0 |
0 |
0 |
|
0 |
0 |
2 |
|
0 |
0 |
0 |
|
1 |
0 |
1 |
|
2 |
2 |
0 |
|
0 |
0 |
0 |
|
0 |
2 |
0 |
|
3 |
0 |
0 |
|
0 |
0 |
0 |
|
0 |
0 |
0 |
|
0 |
0 |
0 |
|
0 |
1 |
1 |
|
0 |
0 |
1 |
|
1 |
0 |
1 |
|
0 |
1 |
0 |
|
0 |
0 |
2 |
|
1 |
5 |
0 |
|
2 |
2 |
0 |
|
0 |
0 |
0 |
|
0 |
0 |
0 |
|
0 |
1 |
0 |
|
0 |
1 |
1 |
|
0 |
0 |
0 |
|
1 |
1 |
1 |
|
1 |
0 |
0 |
|
1 |
0 |
0 |
| Total |
71 |
14 |
7 |
|
17 |
16 |
16 |
| Mean |
2.54 |
0.50 |
0.25 |
|
0.61 |
0.57 |
0.57 |
| Std Dev |
11.29 |
1.11 |
0.84 |
|
0.74 |
0.84 |
0.74 |
We have an outlier in row 9. A 60 when the mean is 2.54 (including the outlier!). This is nearly 5.1 standard deviations above the mean. If this sample were part of the same population as the rest of the samples the odds of that happening by chance are about 1 in 5.6 million. I considered keeping this row anyway and ascribe the greater numbers to a much greater set of people known. But the ratios are nowhere close to another other samples. So, for the following discussion I’m going exclude that row. You can easily modify any conclusions on your own if you want to include that row.
This gives us the following simple statistics:
| Totals: |
11 |
14 |
3 |
|
16 |
14 |
16 |
| Mean |
0.41 |
0.52 |
0.11 |
|
0.59 |
0.52 |
0.59 |
| Std Dev |
0.80 |
1.12 |
0.42 |
|
0.75 |
0.80 |
0.75 |
There is still one “vaccine” death report with a 4.46 standard deviation which one could argue is an outlier but I’m leaving it in. The numbers we are dealing with are just so small that things may look odd when they really aren’t.
At least 76% of the U.S. population, just under 250,000,000 people, has had at least one dose of one of the vaccines.
Our sample is 27 people. A rough rule of thumb is that people knows about 600 people. I think that is a bit high but let’s go for it since it is based on some evidence as opposed to my gut feel from a collection of other sources (sociological issues develop when the groups get larger than about 200). But knowing 600 is different than having a tribe of 600.
With this information our 27 reports represents about 16,200 people.
This means that the odds of dying from COVID-19 is about 16 / 16,200 or 0.099%. This is not the odds of dying once you were infected. This is the odds of dying after living through two years of the pandemic and taking whatever precautions, including vaccinations, these people engaged in.
The odds of dying from the “vaccine” is about 3 / 12,312 or 0.024%. This set of people deliberately exposed themselves to this risk which is about 1/5 the risk of dying if they were to continuing using whatever precautions to avoid being infected and dying of COVID. But these numbers are too small to have much reliability. A difference of just one less or one more changes the odds to 0.016% or 0.032%. Even the higher number is less than one third the risk of a COVID death.
This does not take into account the apparent higher risk of vaccination for young people and the higher risk of COVID death for old people and other risk factors such as obesity, etc. At some point on the curves we are likely to see the tradeoffs cross over. It is easy to imagine that a young healthy person has a higher risk of death from the vaccine than from taking some precautions and risking a COVID infection and death. Also not taken into account is the probable higher vaccination rate in those with higher risk factors.
For the long term adverse effects the odds are worse for the “vaccine” than COVID. They are 14 / 16,200 versus 14 / 12,312 or 0.086% versus 0.113%. The same caveats as in the death rates apply.
The ER/hospitalization rates are 16 / 16,200 and 11 / 12,312 or 0.099% and 0.089%. Or for our sample size, essentially the same.
My conclusions from this is that the average risk of death from COVID is significantly greater than the average risk of death from the “vaccines”. The average long term adverse effects (known at this time) are a little higher.
I have had the two Moderna shots early last year and the booster shot 10 days ago. The side effects for the first two shots were a sore arm for several days, and chills, fever and low energy for one day. The booster effect were a slightly sore arm and slightly lower energy for one day. Barb’s experience with Pfizer were essentially the same for the first two and somewhat greater than mine for the booster a couple months ago. I’m confident we made the right decisions for us. Our risk of death is now much lower and we escaped the known adverse effects of the shots.
Make the best decision you can for yourself.
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