The best form of deception is partial truths whether intentional or not.
Look at the data sources and you will see that the percentage of vaccine uptake is displayed on the same graph as the percentage of that vaccine groups representation of all cause mortality.
Had the vaccinated been given pure water the vaccine uptake percentage lines and the proportion of the population represented by the vaccine uptake range would have indeed been the same.
That's the whole point; they are not the same and they are consistently not the same for one dose , two dose and three dose charts and data. In the related Substack for the tiny 0.5% of the all cause deaths that the 18-39 cohort population represent while the same cohort represents 40% of the total 18+ cohort population (Over 19 million people) you see the SAME disproportionate all-cause death charts and Data; AND AGAIN INCEASING WITH DOSE
There is no way for this 18-39 Cohort that one can claim their 0.5% of all cause deaths affected the other 99.5% of all cause deaths in a significant way.
On top of that, even if one DID try to apply the "disproportionate uptake of vaccine" line to say "we are not seeing what we are seeing" you are then faced with everybody seeing what they are seeing even in the young 18-39 year cohort in the subsequent Substack See:
Its not about feelings or bad experiences with some other partial data set from Germany, or from wherever, and its not about applying labels that simply don't hold like "Simpson's paradox"
"Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined"
Because the "trend" remained in the 18-39 group as with the"18+ (Combined group).
AND IT MATCHED THE COMMON SENSE HYPOTHESIS
The Substacks are completely transparent and the data sources and working files are there for ALL to review , download and obtain still further charts. There are NO assumptions of vaccine uptake there is just charting of the ACTUAL vaccine uptakes and charting of the ACTUAL corresponding representation of the UPTAKE group's percentage representation of all cause mortality.
The wonderful part of the "England" data is there is no more hiding the Vaccine Uptake figures that were "buried" in the "public domain" in a most obscure inclusion into an ONS data file of a UKHSA Forthnightly National Influenza and COVID-19 Report.
The ONS data file was supposed to represent the data of "Figure 19".
From that figure; one would NEVER have been able to guess that tab of Data in the ONS file for this figure contained the entire England Vaccine Uptake by age groups from the beginning Jan 2021 up until 2nd of July 2023 which more than handsomely covered the period of the ONS All cause mortality Data. It was a cat out of the bag data set!!
I dare say if you could find the same two matching data sources for your Germany data ,which you clearly do not have; hence the wild paradox that you can produce , we could apply the plausible Hypothesis to the properly understood and graphed German data.
See if you can find the data sets and hence eliminate your need for so many assumption and give up on the idea that a Paradox can be allowed to exist when you have the two necessary data sets to hand.
The assumptions are accurate in this case, and reveal a significant bias when age groups are aggregated. The result is therefore invalid. With finer age grouping, the error weakens, but in principle persists. The RRs then reduce to about 1.5.
An RR of 1.5 in a population with e.g. 80% vaccination rate would mean an excess mortality of 40%. Much too high. One can conclude that the 1.5 still includes an overestimation.
As I said, I share your view that these vaccinations were involved in the excess mortality. But not in this amount.
You claim this, but have not proven it. I have shown that the assumption of a Simpson bias is correct. Currently I am working on a program to calculate this bias for England as a function of cohort size. One problem is that there are two different data sets on the English vaccination rates. As an aside, it has been confirmed that the death data is not very reliable either. Norman Fenton mentioned among other things that from these data there is a shift in the non covid death rates of the unvaccinated, which should not be.
Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined.
There is no reversal at all - there is a strong Mirroring of the phenomenon
It is immaterial if Norman Fenton or Bill Gates mentions something about a subset of data like non-covid deaths in the unvaccinated - with all cause death data it has become very difficult for the data to be hidden.
Your aside and additional unsubstantiated comment that the death data is not reliable is also meaningless. As is your comment about the data coming from two different sets ;
“Published mortality statistics are based on deaths registered in England and Wales, so include some deaths of residents of other UK countries and of visitors, where the death occurred in England and Wales.”
To see the extent of how this might affect the comparative populations it then produces a table that shows this will affect the number of deaths reported by between 0.1 and 0.2 %
When you "calculate a bias" - what you need to show is the data you have referred to from the data sources presented in the analysis. There's no more hiding behind modelling now because we have the data.
The exposed England data we have charted allows for the isolation of narrow age groups
18-39, 40-49, 50-59, 60-69,70-79, 80-89 or 90+
Ulf take your pick and chart as I have any age group you see and see if you can find a reversal
A Calculated Bias will be seen as just that a calculated bias to muddy very clear waters
The mRNA disaster has to be stopped and you should be doing everything in your power to re-enforce that message
Maybe I will post something about this, but wouldn't it be your job to get rid of this errors? You won't fight any disaster ever with dubious results. If you give an address, I can send some figures.
Ulf, I'm not sure what errors you are referring to however in both substacks there is access to all the data files and further their is access to those same data files showing extra columns in the relevant worksheet where the data was extracted.
To make things completely transparent an excel file , that compiled the extracted data and then generated the charts, is ALSO on the substacks.
This makes it possible for anyone to pick apart the workings with the actual data to hand.
If you wish to examine any age group 18-39, 40-49, 50-59, 60-69,70-79, 80-89 or 90+ and look for your Paradox it is possible - common sense and the examination of the lowest resolution age group of 18-39 (I say lowest resolution because the number of all-cause deaths in that age group 18-39 represents only 0.5% of the all cause deaths in the 18+ cohort) show us clearly there is no Simpson's paradox or any other form of what you see is not what you are seeing phenomenon
For those unfamiliar with this "handy term" thrown around more often by those looking to discredit data with modeling and statistical theories rather than to examine the data to hand thoroughly; a Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined.
Comparing the 18+ charts and findings covering the groups:
18-39, 40-49, 50-59, 60-69,70-79, 80-89 or 90+
And then drilling down to the Youngest subset of that group the 18-39 intentionally because there is always going to be the age bias claim by those hoping the trend is not there we clearly se the trend does NOT reverse
There is a strong Mirroring of the phenomenon
Please feel free to post some numbers on your substack but make sure you make clear reference to where you extracted the numbers from the data sets to hand; the England data sets that have been brought to the surface of the Public Domain. Like in the Substacks you are critiquing make your workings - excel sheets available by dragging them into the post.
It is our job to get rid of dangerous risks to health (Especially the health of our parents and our children); this is a doubled down responsibility - especially when there is a mass injected Novel technology product where there is a strong and clear hypothesis that the products can and will cause significant harm and there are data signals screaming at us from all directions that include; record excess deaths, record reductions in birth rates, record reports of adverse events per dose of any vaccine across the board of highly attenuated reporting systems, record numbers of doctors coming forward with vaccine horror stories, record numbers of pilots, sports stars, public figures collapsing at public events....
Ulf, please focus on if there is a loaded gun pointing at our parents and our children and not on how clean a loaded gun can where we all can forget the clear and present danger.
The data is there for you to make specific calculations in any age group you like
The best form of deception is partial truths whether intentional or not.
Look at the data sources and you will see that the percentage of vaccine uptake is displayed on the same graph as the percentage of that vaccine groups representation of all cause mortality.
Had the vaccinated been given pure water the vaccine uptake percentage lines and the proportion of the population represented by the vaccine uptake range would have indeed been the same.
That's the whole point; they are not the same and they are consistently not the same for one dose , two dose and three dose charts and data. In the related Substack for the tiny 0.5% of the all cause deaths that the 18-39 cohort population represent while the same cohort represents 40% of the total 18+ cohort population (Over 19 million people) you see the SAME disproportionate all-cause death charts and Data; AND AGAIN INCEASING WITH DOSE
There is no way for this 18-39 Cohort that one can claim their 0.5% of all cause deaths affected the other 99.5% of all cause deaths in a significant way.
On top of that, even if one DID try to apply the "disproportionate uptake of vaccine" line to say "we are not seeing what we are seeing" you are then faced with everybody seeing what they are seeing even in the young 18-39 year cohort in the subsequent Substack See:
https://open.substack.com/pub/thenobodywhoknowseverybody/p/further-proof-the-age-adjusted-argument
Its not about feelings or bad experiences with some other partial data set from Germany, or from wherever, and its not about applying labels that simply don't hold like "Simpson's paradox"
"Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined"
Because the "trend" remained in the 18-39 group as with the"18+ (Combined group).
AND IT MATCHED THE COMMON SENSE HYPOTHESIS
The Substacks are completely transparent and the data sources and working files are there for ALL to review , download and obtain still further charts. There are NO assumptions of vaccine uptake there is just charting of the ACTUAL vaccine uptakes and charting of the ACTUAL corresponding representation of the UPTAKE group's percentage representation of all cause mortality.
The wonderful part of the "England" data is there is no more hiding the Vaccine Uptake figures that were "buried" in the "public domain" in a most obscure inclusion into an ONS data file of a UKHSA Forthnightly National Influenza and COVID-19 Report.
The ONS data file was supposed to represent the data of "Figure 19".
From that figure; one would NEVER have been able to guess that tab of Data in the ONS file for this figure contained the entire England Vaccine Uptake by age groups from the beginning Jan 2021 up until 2nd of July 2023 which more than handsomely covered the period of the ONS All cause mortality Data. It was a cat out of the bag data set!!
I dare say if you could find the same two matching data sources for your Germany data ,which you clearly do not have; hence the wild paradox that you can produce , we could apply the plausible Hypothesis to the properly understood and graphed German data.
See if you can find the data sets and hence eliminate your need for so many assumption and give up on the idea that a Paradox can be allowed to exist when you have the two necessary data sets to hand.
The assumptions are accurate in this case, and reveal a significant bias when age groups are aggregated. The result is therefore invalid. With finer age grouping, the error weakens, but in principle persists. The RRs then reduce to about 1.5.
See: https://tkp.at/2023/06/21/englische-todeszahlen-zu-geimpften-und-ungeimpften-zeigen-es-war-die-c19-impfung/
An RR of 1.5 in a population with e.g. 80% vaccination rate would mean an excess mortality of 40%. Much too high. One can conclude that the 1.5 still includes an overestimation.
As I said, I share your view that these vaccinations were involved in the excess mortality. But not in this amount.
Stay tuned ULF the same shows up with Deaths involving Covid-19
There's no muddying the waters with RRs
Your assumptions are wrong
You claim this, but have not proven it. I have shown that the assumption of a Simpson bias is correct. Currently I am working on a program to calculate this bias for England as a function of cohort size. One problem is that there are two different data sets on the English vaccination rates. As an aside, it has been confirmed that the death data is not very reliable either. Norman Fenton mentioned among other things that from these data there is a shift in the non covid death rates of the unvaccinated, which should not be.
Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined.
There is no reversal at all - there is a strong Mirroring of the phenomenon
18+ With Groups Combined
https://open.substack.com/pub/thenobodywhoknowseverybody/p/proof-of-a-mrna-disaster-a-buried
18-39 (youngest Group) and representing a tiny 0.5% subset of the combined groups all cause death
https://thenobodywhoknowseverybody.substack.com/p/further-proof-the-age-adjusted-argument
It is immaterial if Norman Fenton or Bill Gates mentions something about a subset of data like non-covid deaths in the unvaccinated - with all cause death data it has become very difficult for the data to be hidden.
Your aside and additional unsubstantiated comment that the death data is not reliable is also meaningless. As is your comment about the data coming from two different sets ;
“Published mortality statistics are based on deaths registered in England and Wales, so include some deaths of residents of other UK countries and of visitors, where the death occurred in England and Wales.”
To see the extent of how this might affect the comparative populations it then produces a table that shows this will affect the number of deaths reported by between 0.1 and 0.2 %
When you "calculate a bias" - what you need to show is the data you have referred to from the data sources presented in the analysis. There's no more hiding behind modelling now because we have the data.
The exposed England data we have charted allows for the isolation of narrow age groups
18-39, 40-49, 50-59, 60-69,70-79, 80-89 or 90+
Ulf take your pick and chart as I have any age group you see and see if you can find a reversal
A Calculated Bias will be seen as just that a calculated bias to muddy very clear waters
The mRNA disaster has to be stopped and you should be doing everything in your power to re-enforce that message
Maybe I will post something about this, but wouldn't it be your job to get rid of this errors? You won't fight any disaster ever with dubious results. If you give an address, I can send some figures.
Ulf, I'm not sure what errors you are referring to however in both substacks there is access to all the data files and further their is access to those same data files showing extra columns in the relevant worksheet where the data was extracted.
To make things completely transparent an excel file , that compiled the extracted data and then generated the charts, is ALSO on the substacks.
This makes it possible for anyone to pick apart the workings with the actual data to hand.
If you wish to examine any age group 18-39, 40-49, 50-59, 60-69,70-79, 80-89 or 90+ and look for your Paradox it is possible - common sense and the examination of the lowest resolution age group of 18-39 (I say lowest resolution because the number of all-cause deaths in that age group 18-39 represents only 0.5% of the all cause deaths in the 18+ cohort) show us clearly there is no Simpson's paradox or any other form of what you see is not what you are seeing phenomenon
For those unfamiliar with this "handy term" thrown around more often by those looking to discredit data with modeling and statistical theories rather than to examine the data to hand thoroughly; a Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined.
Comparing the 18+ charts and findings covering the groups:
18-39, 40-49, 50-59, 60-69,70-79, 80-89 or 90+
And then drilling down to the Youngest subset of that group the 18-39 intentionally because there is always going to be the age bias claim by those hoping the trend is not there we clearly se the trend does NOT reverse
There is a strong Mirroring of the phenomenon
Please feel free to post some numbers on your substack but make sure you make clear reference to where you extracted the numbers from the data sets to hand; the England data sets that have been brought to the surface of the Public Domain. Like in the Substacks you are critiquing make your workings - excel sheets available by dragging them into the post.
It is our job to get rid of dangerous risks to health (Especially the health of our parents and our children); this is a doubled down responsibility - especially when there is a mass injected Novel technology product where there is a strong and clear hypothesis that the products can and will cause significant harm and there are data signals screaming at us from all directions that include; record excess deaths, record reductions in birth rates, record reports of adverse events per dose of any vaccine across the board of highly attenuated reporting systems, record numbers of doctors coming forward with vaccine horror stories, record numbers of pilots, sports stars, public figures collapsing at public events....
Ulf, please focus on if there is a loaded gun pointing at our parents and our children and not on how clean a loaded gun can where we all can forget the clear and present danger.
The data is there for you to make specific calculations in any age group you like