I'll show you the easiest way to do that in this article. However, it doesn't appear that any of the New Zealand lawmakers or health authorities are interested in what the data shows.
I have just downloaded the BY data from your github portal. It took four bites of about 1-million records each, loaded into different Libre Office spreadsheet to get it all. I also have the Chris Johnson FOIA data dump. I would say none of them are easy to handle because they are in a form that still requires knowledge of demographics and vaccination records to to get meaningful data and the NZ MOH doesn't want usto know essential details. I'm not a R programmer, so I appreciate you writing the code to extract useful reports. Someone needs to re-check Scoop McGoo's work because there are methodology errors. The report by US Mortality substack writer did a reasonable job, but it is clouded by uncertainties in population splits of jabbed and unjabbed and the dreaded "<5" excuse to hide true death counts.. There is no doubt ther
There is no doubt there was a leap in deaths in the older cohorts, starting in May 2021 continuing until November, when there was zero Covid and strict isolation of borders and the elderly within nursing homes, retirement villages and private homes. The same conditions as 2020. The situation compounded in 2022 after boosters and then Covid. I have overlaid the chart of deaths due to covid on the chart of all cause mortality and between and between April and June 2022 the lines diverge. On the week of Jume 2nd for instance, there were 839 deaths, yet only 72 due to Covid, leaving 767 deaths due to other causes. The trend line for this week predicted only 640-650 deaths.
These are the blueblood Canaanites .they have always been with us…they own the World and are pushing depopulation and their New World Order. They also worship Satan, Baal and Moloch. They murder.our children for adrenochrome because they have lost their connection to God.
Your analysis is incorrect. If the batches were all the same you would expect the histogram to be approximately even across all batches for a particular age group a uniform distribution, not a normal distribution. From mere observation of the variance in the distribution away from uniform you can conclude the batches are not equal.
You have the correct conclusion but are using the wrong basis. The statistical test should be whether the variance between batches is too great to represent a uniform distribution. There are insufficient age groups to enable anything resembling the central limit theorem, which could only tell you the the mean and variance of the sample means and variances would be normally distributed, if you were to have over 35 categories, which you don't.
"If the batches were all the same you would expect the histogram to be approximately even across all batches for a particular age group a uniform distribution, not a normal distribution."
no that's not how it works. It approaches a normal distribution per the central limit theorem. You are measuring the same mean value of the same population since all the vaccines are perfectly safe as everyone knows.
If you create simulated data with 100,000 people who are divided into 100 batches of 1,000 people, and you kill 1,000 random people, the histogram for deaths per batch won't be uniform. But it will roughly follow a normal distribution:
As We Warned Long Ago...Up To 400 Billion Foreign DNA Fragments In EACH Covid Injection Pose HUGE Integration Potential - These DNA Pieces Ride WITH LNP-Coated MRNA And Go Right Into The Body Cells - The Sacred Human Genome Is Likely Being Massively Integrated With 100s Of Billions Of Planted DNA Pieces - The Death Jabs Are Probably FAR More Deadly Than Thought - FDA Admits It Never Tested For This!
Something is weird about the data if they are annual all-cause mortality data.
For simplicity look at the age group 64-69 for the highest death rate batch. It is 7.32 per 1000. The normal death rate for this age group should be 9.9 per 1,000.
Yes, the healthy vacinee effect, in that those who were literally on death's doorstep did not get the jabs, because they were deemed unlikely to survive them. Let me illustrate with a hypothetical example of how much the healthe status of a cohort can affect the overall results:
Consider the following hypothetical scenario regarding people who do or do not get a treatment that is deemed risky to administer to people who are in very feeble health:
Group A: 100,000 person-years, 110 deaths
Group B: 100,000 person-years, 90 deaths
Looks like Group B did much better, right? Not so fast…
Say Group A included 10,000 individuals with very serious health conditions, 100 of whom died during the analyzed period, but only 1,000 of Group B were in comparably unhealthy condition, 60 of whom died during the analyzed period.
Then, among the healthy subsets, Group A had 10 deaths among 90,000 person-years, or 11.11 deaths per 100K person-years, as compared to Group B, which had 30 deaths among 99,000 person-years, or 30.30 deaths per 100K person-years, which is 2.73 times the Group A death rate.
Then, among the unhealthy subsets, Group A had 100 deaths among 10,000 person-years, or 1,000 deaths per 100K person-years, as compared to Group B, which had 60 deaths among 1,000 person-years, or 6,000 deaths per 100K person-years, which is 6 times the Group A death rate.
So, that is how a treatment such as a vaccine that increases on'e likelihood of death by several times over can appear to decrease one's likelihood of death, simply by giving it to a cohort of people who are in much better baseline health.
But that cannot be masked by all cause death rates, which one can't so easily categorize around..
Vaccinated people often have lower than baseline mortality because of the healthy vaccinee effect. The number of deaths is reduced even further during approximately the first 2-3 months after vaccination because of the temporal/time-varying HVE, so people spent a fairly large part of the 1-year observation period being impacted by the temporal HVE.
In the Czech record-level data unvaccinated had much higher ASMR than vaccinated people, so when my baseline was derived from the mortality rates among the total Czech population which also includes unvaccinated people, most batches got negative age-normalized excess mortality: sars2.net/czech3.html#Batch_study_by_authors_from_Palack_University.
In the NZ data I got -32% excess mortality for batch 34 and 8% excess mortality for batch 38: docs.google.com/spreadsheets/d/126_3eU6Vq6IOFr8SMq3rnbv5rrkN0kPYIyy_yBZmQ4g. I calculated the baseline mortality rate by taking a weighted averages of the total mortality rates for each age in 2021-2023 where the weight was the number of person-days under each age that were included in Barry's dataset.
So the total excess mortality of batch 38 was only about 8% above the baseline, so it's not unexpected for individual age groups to have a mortality rate below the baseline.
I also got negative excess mortality for batch 38 in ages 60-69:
Kirsch's mortality rate in ages 80-84 was about 0.0635 for batch 38 and 0.0252 for batch 34. So even batch 38 was only about 13% above the baseline, even though most of the observation period was in the year 2022 when there was elevated mortality because of COVID.
Another thing you probably missed is that Kirsch took the age of each person from the age column of his 4M CSV file, which is the age on December 2nd 2023 for people who didn't die or the day of death for people who died (which was in both cases calculated incorrectly as a floored division of the age in days by 365). However the age in the age column is on average about 1.4 years higher than the age of people during the 1-year observation period.
In 2015-2019 the total mortality rate was about 1041 deaths per 100,000 people for ages 65-69, 948 for ages 64-68, and 868 for ages 63-67. So the average of the last two values is about 17% lower than the first value.
In the following code I treated the age of each person as their age 182 days after vaccination, which was roughly the average age during the 1-year observation period. I used the total mortality rate of each age group in 2015-2019 as the baseline. However I still got negative excess mortality for ages 65-69 in batch 38:
I must be getting bolder, or caring less. This is the second Kirsch email I have forwarded to my sons. I don’t know what I’m afraid of anymore, I’ve completely lost my tribe. It’s a good thing, albeit painful.
Podcasters Joe Rogan and Michael Malice discussed last week why the corporate press continues to dismiss ways doctors can treat Covid-19 aside from vaccination.
Rogan said that Dr. Pierre Kory from the Front Line Critical Care Covid group treated him and hundreds of members of Congress with monoclonal antibodies, prednisone, z-pak, NAD, vitamins, and ivermectin.
"By the way, 200 Congresspeople have been treated with Ivermectin for Covid. Google that. You can probably find that in Dr. Pierre Kory's Twitter page," Rogan said. "Before there were vaccines, this was a common off-label treatment for Covid."
Steve, you should know not to baffle those who point to "the science" to justify robbing taxpayers all over the world whilst taking away their cheap reliable energy, fertilizer for food security and secure unlimited transportation fuels with REAL science-based Math... They don't have the capacity to understand either the math or it's implications, and even the few who might will either hide it or dredge up some obscure total BS "reason" to discount/discredit it...
Such is the nature of "ideology over facts" individuals... Ever noticed how people like Oprah encourage their acolytes to speak "your" truth rather than THE truth??? It's all agenda driven for the saliva-flecked minion zealots and their satanic globalist overlords... Truth - real truth - has no sway over them even if they are able to see it... Sadly, the words of Jesus in John 8 : 44 - 45 are proving to be the unfolding world-wide scenario for all who have swallowed the globalist lies... "You belong to your father, the devil, and you want to carry out his desires. He was a murderer from the beginning, refusing to uphold the truth, because there is no truth in him. When he lies, he speaks his native language, because he is a liar and the father of lies. 45But because I speak the truth, you do not believe Me!…" And they don't and won't believe people like you either Steve... The truth you speak is just basic math and plain for any with half a brain to see... Sadly, a black heart will always hold sway over a white brain...
But please keep putting it out there Steve, for when God changes a heart, the simple, diabolical truth about what has been done to humanity that you expose will overwhelm some, and hopefully lead to humility and regret and reparative joining of shoulders to the wheel of truth... So God speed and God's blessing continue upon you mate... May the truth ever have your light shining upon it....
Philippines had 3 main spikes in excess deaths around April 2021, September 2021, and January 2022, but all of them coincided with a spike in COVID deaths and a spike in PCR positivity rate: i.ibb.co/NVsvSpD/philippines-owid-splines.png.
The contingency matrix used for the Fisher's test is supposed to look like this, but Kirsch's list accidentally included a 5th number which didn't belong to the matrix:
group survived died
batch_34 5381 139
batch_38 2859 194
Fisher's test calculates the likelihood that there could've been the given number of deaths and given number of survived people by chance if the deaths would've been distributed randomly across the two batches. But the reason why the likelihood is so low is because the deaths were not actually distributed randomly.
"MR bucket" means a range of mortality rates, like for example the first bar includes mortality rates in the range of 0.0082 to 0.0102 deaths per person over a period of one year. So the values are not percentages.
I was reading your comment to Steve and to let you know that Barry's court case is in April next year around the 21st or the 23rd. Not sure on the date. Yes Barry is a HERO in my eyes and I'm so grateful to him. If I hear anyone go after him at all I DO TELL them the TRUTH of what the NZ government did and still doing to the people of NZ. I won't let anyone speak bad of Barry. He will go down in the history books for what he has done. Just thinking about what they have put Barry through it breaks my heart. Barry is a gentle beautiful man and I respect him so much. Sometimes the road is a lonely place when your out there fighting for freedom and what is right. I know but will never ever give up. I think of my children and my grandchildren and know I'm doing it for them. Plus my animals. LOL..
I hope you and your family are well. Take care. With love 🧡 A friend from New Zealand. 🇳🇿
We are meant to comply and not think for ourselves just accept the illness's they cause and to crawl away and not make a fuss just die, as is the plan by the Globalists.
The percentage of the U.S. predicted to be depopulated in 2025 per the "Deagel Depopulation Prediction" is 68.5%. I think it was written by Edwin Deagel in the mid 60's and that means this event has been well-planned if this isn't a "coincidence".
Perhaps the "Deagel Depopulation Prediction" was actually a "Deagel Pepopulation Plan" and the COVID vaccinated are the people they have chosen to depopulate?
Hi Steve
I have just downloaded the BY data from your github portal. It took four bites of about 1-million records each, loaded into different Libre Office spreadsheet to get it all. I also have the Chris Johnson FOIA data dump. I would say none of them are easy to handle because they are in a form that still requires knowledge of demographics and vaccination records to to get meaningful data and the NZ MOH doesn't want usto know essential details. I'm not a R programmer, so I appreciate you writing the code to extract useful reports. Someone needs to re-check Scoop McGoo's work because there are methodology errors. The report by US Mortality substack writer did a reasonable job, but it is clouded by uncertainties in population splits of jabbed and unjabbed and the dreaded "<5" excuse to hide true death counts.. There is no doubt ther
There is no doubt there was a leap in deaths in the older cohorts, starting in May 2021 continuing until November, when there was zero Covid and strict isolation of borders and the elderly within nursing homes, retirement villages and private homes. The same conditions as 2020. The situation compounded in 2022 after boosters and then Covid. I have overlaid the chart of deaths due to covid on the chart of all cause mortality and between and between April and June 2022 the lines diverge. On the week of Jume 2nd for instance, there were 839 deaths, yet only 72 due to Covid, leaving 767 deaths due to other causes. The trend line for this week predicted only 640-650 deaths.
Me and Uncle John Returns have already checked the work by Scoops McGoo:
x.com/search?q=from%3AUncleJo46902375%20%40sco0psmcgoo&f=live
x.com/search?q=from%3Amongol_fi%20%40sco0psmcgoo&f=live
sars2.net/moar.html
These are the blueblood Canaanites .they have always been with us…they own the World and are pushing depopulation and their New World Order. They also worship Satan, Baal and Moloch. They murder.our children for adrenochrome because they have lost their connection to God.
I independently ran these numbers using methods approved of by CDC and found -- for older groups at least -- highly-significant batch variation in mortality [ https://deepd1ve.substack.com/p/independently-confirmed-nz-batch ].
Your analysis is incorrect. If the batches were all the same you would expect the histogram to be approximately even across all batches for a particular age group a uniform distribution, not a normal distribution. From mere observation of the variance in the distribution away from uniform you can conclude the batches are not equal.
You have the correct conclusion but are using the wrong basis. The statistical test should be whether the variance between batches is too great to represent a uniform distribution. There are insufficient age groups to enable anything resembling the central limit theorem, which could only tell you the the mean and variance of the sample means and variances would be normally distributed, if you were to have over 35 categories, which you don't.
"If the batches were all the same you would expect the histogram to be approximately even across all batches for a particular age group a uniform distribution, not a normal distribution."
no that's not how it works. It approaches a normal distribution per the central limit theorem. You are measuring the same mean value of the same population since all the vaccines are perfectly safe as everyone knows.
If you create simulated data with 100,000 people who are divided into 100 batches of 1,000 people, and you kill 1,000 random people, the histogram for deaths per batch won't be uniform. But it will roughly follow a normal distribution:
> d=data.frame(batch=rep(1:100,each=1000),dead=F)
> d$dead[sample(1e5,1e3)]=T
> c=tapply(d$dead,d$batch,sum)
> c=table(factor(c,0:max(c)))
> writeLines(paste0(names(c),":",c,collapse=" "))
0:0 1:0 2:0 3:1 4:1 5:5 6:8 7:10 8:12 9:11 10:7 11:10 12:10 13:11 14:5 15:5 16:3 17:0 18:0 19:1
Tucker and FL surgeon general:
As We Warned Long Ago...Up To 400 Billion Foreign DNA Fragments In EACH Covid Injection Pose HUGE Integration Potential - These DNA Pieces Ride WITH LNP-Coated MRNA And Go Right Into The Body Cells - The Sacred Human Genome Is Likely Being Massively Integrated With 100s Of Billions Of Planted DNA Pieces - The Death Jabs Are Probably FAR More Deadly Than Thought - FDA Admits It Never Tested For This!
https://x.com/Humanspective/status/1834792287819514365
When you're a disarmed pasifist in the pocket of big Harma...
Something is weird about the data if they are annual all-cause mortality data.
For simplicity look at the age group 64-69 for the highest death rate batch. It is 7.32 per 1000. The normal death rate for this age group should be 9.9 per 1,000.
What am I missing?
Yes, the healthy vacinee effect, in that those who were literally on death's doorstep did not get the jabs, because they were deemed unlikely to survive them. Let me illustrate with a hypothetical example of how much the healthe status of a cohort can affect the overall results:
Consider the following hypothetical scenario regarding people who do or do not get a treatment that is deemed risky to administer to people who are in very feeble health:
Group A: 100,000 person-years, 110 deaths
Group B: 100,000 person-years, 90 deaths
Looks like Group B did much better, right? Not so fast…
Say Group A included 10,000 individuals with very serious health conditions, 100 of whom died during the analyzed period, but only 1,000 of Group B were in comparably unhealthy condition, 60 of whom died during the analyzed period.
Then, among the healthy subsets, Group A had 10 deaths among 90,000 person-years, or 11.11 deaths per 100K person-years, as compared to Group B, which had 30 deaths among 99,000 person-years, or 30.30 deaths per 100K person-years, which is 2.73 times the Group A death rate.
Then, among the unhealthy subsets, Group A had 100 deaths among 10,000 person-years, or 1,000 deaths per 100K person-years, as compared to Group B, which had 60 deaths among 1,000 person-years, or 6,000 deaths per 100K person-years, which is 6 times the Group A death rate.
So, that is how a treatment such as a vaccine that increases on'e likelihood of death by several times over can appear to decrease one's likelihood of death, simply by giving it to a cohort of people who are in much better baseline health.
But that cannot be masked by all cause death rates, which one can't so easily categorize around..
Vaccinated people often have lower than baseline mortality because of the healthy vaccinee effect. The number of deaths is reduced even further during approximately the first 2-3 months after vaccination because of the temporal/time-varying HVE, so people spent a fairly large part of the 1-year observation period being impacted by the temporal HVE.
In the Czech record-level data unvaccinated had much higher ASMR than vaccinated people, so when my baseline was derived from the mortality rates among the total Czech population which also includes unvaccinated people, most batches got negative age-normalized excess mortality: sars2.net/czech3.html#Batch_study_by_authors_from_Palack_University.
In the NZ data I got -32% excess mortality for batch 34 and 8% excess mortality for batch 38: docs.google.com/spreadsheets/d/126_3eU6Vq6IOFr8SMq3rnbv5rrkN0kPYIyy_yBZmQ4g. I calculated the baseline mortality rate by taking a weighted averages of the total mortality rates for each age in 2021-2023 where the weight was the number of person-days under each age that were included in Barry's dataset.
A similar calculation is done by this R code:
> library(data.table)
> b=fread("http://sars2.net/f/bucketsbatchkeep")
> a=b[,.(dead=sum(dead),pop=sum(alive)),.(age=pmin(age,95),batch)]
> pop=fread("http://sars2.net/f/nzpopdead.csv")
> a=merge(pop[year%in%2021:2023,.(base=sum(dead)/sum(pop,na.rm=T)),.(age=pmin(age,95))],a)[,base:=base*pop/365]
> a[batch%in%c(34,38),.(excesspct=(sum(dead)/sum(base)-1)*100),batch]|>print(r=F)
batch excesspct
38 7.992103
34 -31.960710
So the total excess mortality of batch 38 was only about 8% above the baseline, so it's not unexpected for individual age groups to have a mortality rate below the baseline.
I also got negative excess mortality for batch 38 in ages 60-69:
> a[batch==38,.(excesspct=(sum(dead)/sum(base)-1)*100),.(age=age%/%10*10)]|>print(r=F)
age excesspct
0 -100.000000
10 43.240391
20 -34.224133
30 -38.213705
40 -14.755000
50 -15.611952
60 -6.656759 # ages 60 to 69
70 14.668249
80 18.634481
90 10.690141
Also in 2015-2019, the total mortality rate of ages 80-84 was about 0.0560 deaths per year:
> pop=fread("http://sars2.net/f/nzpopdead.csv")
> pop[year%in%2015:2019&age%in%80:84,sum(dead)/sum(pop)]
[1] 0.05599301
Kirsch's mortality rate in ages 80-84 was about 0.0635 for batch 38 and 0.0252 for batch 34. So even batch 38 was only about 13% above the baseline, even though most of the observation period was in the year 2022 when there was elevated mortality because of COVID.
Another thing you probably missed is that Kirsch took the age of each person from the age column of his 4M CSV file, which is the age on December 2nd 2023 for people who didn't die or the day of death for people who died (which was in both cases calculated incorrectly as a floored division of the age in days by 365). However the age in the age column is on average about 1.4 years higher than the age of people during the 1-year observation period.
In 2015-2019 the total mortality rate was about 1041 deaths per 100,000 people for ages 65-69, 948 for ages 64-68, and 868 for ages 63-67. So the average of the last two values is about 17% lower than the first value.
In the following code I treated the age of each person as their age 182 days after vaccination, which was roughly the average age during the 1-year observation period. I used the total mortality rate of each age group in 2015-2019 as the baseline. However I still got negative excess mortality for ages 65-69 in batch 38:
> nz=fread("https://github.com/skirsch/NewZealand/raw/main/data/nz-record-level-data-4M-records.csv.gz")
> for(k in grep("date",names(nz)))nz[[k]]=as.Date(nz[[k]],"%m-%d-%Y")
> age=\(x,y){class(x)=class(y)=NULL;(y-x-(y-789)%/%1461+(x-789)%/%1461)%/%365}
> nz[,vaxage:=age(date_of_birth,date_time_of_service+182)]
> a=nz[,.(cmr=sum(date_of_death-date_time_of_service<365,na.rm=T)/.N),.(batch=batch_id,age=agecut(vaxage,ages))]
> a=merge(pop[year%in%2015:2019,.(base=sum(dead)/sum(pop,na.rm=T)),.(age=agecut(age,ages))],a)
> a[batch%in%c(34,38),xtabs(round((cmr/base-1)*100)~age+batch)[,2:1]]
batch
age 38 34
0-39 -25 80
40-44 -41 74
45-49 8 14
50-54 -41 5
55-59 -3 -25
60-64 -4 -35
65-69 -14 -40 # batch 38 has about -14% excess mortality in ages 65-69
70-74 1 -42
75-79 16 -35
80-84 24 -50
85-89 2 -41
90+ 0 -37
I must be getting bolder, or caring less. This is the second Kirsch email I have forwarded to my sons. I don’t know what I’m afraid of anymore, I’ve completely lost my tribe. It’s a good thing, albeit painful.
Hmm? Did you know....
Joe Rogan: Dr. Pierre Kory Said 200 Members of Congress Were Treated With Ivermectin
Posted By Tim Hains
https://www.realclearpolitics.com/video/2021/10/26/joe_rogan_says_dr_pierre_kory_treated_200_members_of_congress_with_ivermectin.html
Podcasters Joe Rogan and Michael Malice discussed last week why the corporate press continues to dismiss ways doctors can treat Covid-19 aside from vaccination.
Rogan said that Dr. Pierre Kory from the Front Line Critical Care Covid group treated him and hundreds of members of Congress with monoclonal antibodies, prednisone, z-pak, NAD, vitamins, and ivermectin.
"By the way, 200 Congresspeople have been treated with Ivermectin for Covid. Google that. You can probably find that in Dr. Pierre Kory's Twitter page," Rogan said. "Before there were vaccines, this was a common off-label treatment for Covid."
Steve, you should know not to baffle those who point to "the science" to justify robbing taxpayers all over the world whilst taking away their cheap reliable energy, fertilizer for food security and secure unlimited transportation fuels with REAL science-based Math... They don't have the capacity to understand either the math or it's implications, and even the few who might will either hide it or dredge up some obscure total BS "reason" to discount/discredit it...
Such is the nature of "ideology over facts" individuals... Ever noticed how people like Oprah encourage their acolytes to speak "your" truth rather than THE truth??? It's all agenda driven for the saliva-flecked minion zealots and their satanic globalist overlords... Truth - real truth - has no sway over them even if they are able to see it... Sadly, the words of Jesus in John 8 : 44 - 45 are proving to be the unfolding world-wide scenario for all who have swallowed the globalist lies... "You belong to your father, the devil, and you want to carry out his desires. He was a murderer from the beginning, refusing to uphold the truth, because there is no truth in him. When he lies, he speaks his native language, because he is a liar and the father of lies. 45But because I speak the truth, you do not believe Me!…" And they don't and won't believe people like you either Steve... The truth you speak is just basic math and plain for any with half a brain to see... Sadly, a black heart will always hold sway over a white brain...
But please keep putting it out there Steve, for when God changes a heart, the simple, diabolical truth about what has been done to humanity that you expose will overwhelm some, and hopefully lead to humility and regret and reparative joining of shoulders to the wheel of truth... So God speed and God's blessing continue upon you mate... May the truth ever have your light shining upon it....
This is an example that the data 'POPS OUT" easily https://www.excessdeathstats.com/philippines/
Philippines had 3 main spikes in excess deaths around April 2021, September 2021, and January 2022, but all of them coincided with a spike in COVID deaths and a spike in PCR positivity rate: i.ibb.co/NVsvSpD/philippines-owid-splines.png.
I'm a simple Finance major with a minor in Electrical Engineering.
I know you're trying to point out differences in various batches.
But, I"m getting tangled up in the weeds. When you get into the
Fisher Matrix being (5381 2859 139 194 8573), I"m totally lost.
It may be very meaningful to a Statitician, but, I'm not familar
with it. I"m also not able to figure out what the Histogram is
attemping to show? The Y-axis is the Mortality Rate of 70-74
year olds, grouped by how many counts of 10 there are? But, it's
supposed to be a 'normal distribution'? The X-axis from the text
says it's the "MR Bucket'? What do you mean by bucket? I see
that there are some various groupings, but, what are the
measurements? So the first histogram count is 8 from the
group of 0.0034 to 0.0054, and the second historgram count is 2
from the group of 0.0054 to 0.0074 etc??? Each x-axis grouping
goes up by a value of 0.0020. What is the X-axis measuring?
Buckets of what? A normal distribution of what occurances? Is
it the Mortality rate percentage? You're dealing with such a
tiny mortality rate percentage, that it's rather meaningless.
The data isn't "POPPING OUT" for me.
The contingency matrix used for the Fisher's test is supposed to look like this, but Kirsch's list accidentally included a 5th number which didn't belong to the matrix:
group survived died
batch_34 5381 139
batch_38 2859 194
Fisher's test calculates the likelihood that there could've been the given number of deaths and given number of survived people by chance if the deaths would've been distributed randomly across the two batches. But the reason why the likelihood is so low is because the deaths were not actually distributed randomly.
"MR bucket" means a range of mortality rates, like for example the first bar includes mortality rates in the range of 0.0082 to 0.0102 deaths per person over a period of one year. So the values are not percentages.
Hello love, 🙋♀️
I was reading your comment to Steve and to let you know that Barry's court case is in April next year around the 21st or the 23rd. Not sure on the date. Yes Barry is a HERO in my eyes and I'm so grateful to him. If I hear anyone go after him at all I DO TELL them the TRUTH of what the NZ government did and still doing to the people of NZ. I won't let anyone speak bad of Barry. He will go down in the history books for what he has done. Just thinking about what they have put Barry through it breaks my heart. Barry is a gentle beautiful man and I respect him so much. Sometimes the road is a lonely place when your out there fighting for freedom and what is right. I know but will never ever give up. I think of my children and my grandchildren and know I'm doing it for them. Plus my animals. LOL..
I hope you and your family are well. Take care. With love 🧡 A friend from New Zealand. 🇳🇿
We are meant to comply and not think for ourselves just accept the illness's they cause and to crawl away and not make a fuss just die, as is the plan by the Globalists.
Aunt Carol is sitting by her phone just waiting for notification she's eligible for her 9th jab....
When I told her they are giving these jabs out hilly-dilly she frowned at me and said "I don't cheat."
hilly-dilly?
Similar to willy-nilly, but uphill.
Steve,
I recently noticed this "coincidence"...
The percentage of the people in the US double-COVID vaccinated is about 70%.
https://usafacts.org/visualizations/covid-vaccine-tracker-states/
The percentage of the U.S. predicted to be depopulated in 2025 per the "Deagel Depopulation Prediction" is 68.5%. I think it was written by Edwin Deagel in the mid 60's and that means this event has been well-planned if this isn't a "coincidence".
https://nobulart.com/wordpress/wp-content/uploads/2021/08/Deagel-2025-Forecast-by-Country.pdf
Perhaps the "Deagel Depopulation Prediction" was actually a "Deagel Pepopulation Plan" and the COVID vaccinated are the people they have chosen to depopulate?
It sure seems like a big "coincidence" to me.
Also Kamala Harris said: “220 million Americans have died from COVID.”
https://citizenwatchreport.com/kamala-harris-220-million-americans-have-died-from-covid-video/ 70% of US population have been vaccinated.
70% of 350 million = 245 million.
https://thehill.com/policy/healthcare/579479-white-house-70-of-adults-are-fully-vaccinated-80-partially/