Consider me as a remedial kindergartener when it comes to Statistics. That disclaimer out of the way, I have learned so much from your articles and conversations. Thank you again Mathew. I am blessed to call you my friend as well as my remedial kindergarten statistics teacher.
Mar 9, 2023·edited Mar 9, 2023Liked by Mathew Crawford
Oh Man. I just read this. Dr. Mikovits was more than correct. She was prescient. The entirety of the formulary process controls of modern mass inoculant production are looking like a major fail. This may be the biggest scandal of all because it impacts all mass vaccine production. It seems they may have been burying unconscionable frequencies of formulary defects and adulteration under the rationale the unbargained for toxic insults stimulate the immune system and function like adjuvants, however toxic or pathological the resulting insult injuring the individual vaccinee who had been lied to about this issue.
The concerns of serial adulteration of the mRNA product class as packaged and sold appears to be potentially so severe and so varied, the net effect of what might also be characterized as a de facto sabotage potentially ruins the presumption of integrity any reasonable person would afford the product performance data and clinical outcomes data arising from mass manufactured vaccines.
Given such a magnitude of vaccine adulteration and formulary defects, should such indeed be the case, there would be an insufficient quality of baseline reliability or integrity.
Any statistical interpolation of such data would unlikely be better than a lone dart thrown arbitrarily into a blank painted sheetrock wall.
I can almost hear them now:
“We did real Science on this. We threw a dart. Look. There it is. 100% effective sticking into that wall. So we got a big grant to throw thousands more darts. Here they are. See. We used lots of data to determine these consequential statistical outcomes set forth in the summary of our research. Experts agree. We the authors declare no conflicting financial interests. It's all shared interest. University administrators get 40% or more of the grant dollars we got. Everybody does it this way. It’s Science."
Then comes a voice from stage left: "What? Somebody died? We got a drug for that too."
Yo. Think. How might one go about manufacturing in ways to make causality of harm and product failure disappear?
Such questions take evil genius to a whole new level.
Mar 9, 2023·edited Mar 9, 2023Liked by Mathew Crawford
Mathew Crawford just nailed it: "Quantitative Narcissism." This best describes the disease pandemic we need to be most concerned about in the modern technological age. The products of this mental health phenomenon are absolutely murderous in their potential to cause pervasive harm to innocent others. The result is serial misuse of human institutions, government and corporate resources in predatory pursuit of narcissistic supply, career security and personal financial gain.
Thanks, Mathew. It appears that HUB is a substantial effect which is almost never taken into account in interpreting data, but crucial to making any conclusions about the meaning of any interventional medical data. What is odd, to me, in the U.S. Mortality data, is that it shows a substantial increase throughout the entire period, including all of 2019.
Thanks Mathew for another great post and video. Really interesting to see your exchange with Seheult and good faith attempt to point him towards better statistical reasoning. He is a difficult case since he does make nice content with clear communication and has a lot of hubris. In his Texas tweet, nice to see a few folks pointing out the problems and inconsistencies. He also fails to mention Texas all-cause excess mortality remained positive all through 2022 as seen in the Texas graph at USMortality https://www.usmortality.com/excess-mortality/percentage, and as you have repeatedly explained, we have both HUB and lack of mean reversion.
Side note: I'm happy to relay that I've finally been able to reproduce your correlation time series graph. You briefly showed your Excel table in the video and this reminded me that you are working with daily data. I made scatter plots behind these correlations and see a lot of zeros for daily mortality because a large number of counties only report deaths on a weekly basis. All of these zeros significantly shrink the correlations into the range [-0.1, 0.1]. For what it's worth, I think if the data are aggregated to weekly or monthly, the correlations become much stronger (e.g. -0.5) and the resulting graph becomes more compelling because then there is no doubt we are dealing with significant associations. I'm glad to pitch in on any data analysis tasks you find are stretching your time.
I watched the video above (Episode I: the overconfidence of Dr. Roger Seheult). Maybe you could be more explicit about what you mean by conditional statistics and healthy user bias with simpler examples. The Monty Hall example is too confusing for most people.
Using examples directly related to vaccines, Covid, excess death etc. is a good idea.
Wikipedia does a good job on Healthy User Bias:
"The kind of subjects that take up an intervention, including by enrolling in a clinical trial, are not representative of the general population. People who volunteer for a study can be expected, on average, to be healthier than people who don't volunteer, as they are concerned for their health and are predisposed to follow medical advice,[1] both factors that would aid one's health. In a sense, being healthy or active about one's health is a precondition for becoming a subject of the study, an effect that can appear under other conditions such as studying particular groups of workers. For example, someone in ill health is unlikely to have a job as manual laborer. As a result, studies of manual laborers are studies of people who are currently healthy enough to engage in manual labor, rather than studies of people who would do manual labor if they were healthy enough."
My main point is multiple simple examples are a good idea.
The Simpson's paradox example you brought up might have gone over many peoples heads.
Here's the Wikipedia explanation for those that don't know what it is:
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.
Consider me as a remedial kindergartener when it comes to Statistics. That disclaimer out of the way, I have learned so much from your articles and conversations. Thank you again Mathew. I am blessed to call you my friend as well as my remedial kindergarten statistics teacher.
Oh Man. I just read this. Dr. Mikovits was more than correct. She was prescient. The entirety of the formulary process controls of modern mass inoculant production are looking like a major fail. This may be the biggest scandal of all because it impacts all mass vaccine production. It seems they may have been burying unconscionable frequencies of formulary defects and adulteration under the rationale the unbargained for toxic insults stimulate the immune system and function like adjuvants, however toxic or pathological the resulting insult injuring the individual vaccinee who had been lied to about this issue.
https://jessicar.substack.com/p/follow-up-on-dna-contamination-of?utm_source=substack&utm_medium=email
The concerns of serial adulteration of the mRNA product class as packaged and sold appears to be potentially so severe and so varied, the net effect of what might also be characterized as a de facto sabotage potentially ruins the presumption of integrity any reasonable person would afford the product performance data and clinical outcomes data arising from mass manufactured vaccines.
Given such a magnitude of vaccine adulteration and formulary defects, should such indeed be the case, there would be an insufficient quality of baseline reliability or integrity.
Any statistical interpolation of such data would unlikely be better than a lone dart thrown arbitrarily into a blank painted sheetrock wall.
I can almost hear them now:
“We did real Science on this. We threw a dart. Look. There it is. 100% effective sticking into that wall. So we got a big grant to throw thousands more darts. Here they are. See. We used lots of data to determine these consequential statistical outcomes set forth in the summary of our research. Experts agree. We the authors declare no conflicting financial interests. It's all shared interest. University administrators get 40% or more of the grant dollars we got. Everybody does it this way. It’s Science."
Then comes a voice from stage left: "What? Somebody died? We got a drug for that too."
Yo. Think. How might one go about manufacturing in ways to make causality of harm and product failure disappear?
Such questions take evil genius to a whole new level.
Mathew Crawford just nailed it: "Quantitative Narcissism." This best describes the disease pandemic we need to be most concerned about in the modern technological age. The products of this mental health phenomenon are absolutely murderous in their potential to cause pervasive harm to innocent others. The result is serial misuse of human institutions, government and corporate resources in predatory pursuit of narcissistic supply, career security and personal financial gain.
Doctors do not understand probability very well as Sebastian Rushworth explains
http://sebastianrushworth.com/2021/06/23/how-well-do-doctors-understand-probability/
This resulted in every positive Covid test result being counted as a ‘case’.
Here, John Hopkins publishes a list of Covid test kits
https://www.centerforhealthsecurity.org/covid-19TestingToolkit/molecular-based-tests/current-molecular-and-antigen-tests.html
Note that some claim 100% sensitivity and 100% specificity. Truly a gold standard test!
Overconfidence in test results ends up in over treatment of patients.
Great result for big Pharma eh!
Thanks, Mathew. It appears that HUB is a substantial effect which is almost never taken into account in interpreting data, but crucial to making any conclusions about the meaning of any interventional medical data. What is odd, to me, in the U.S. Mortality data, is that it shows a substantial increase throughout the entire period, including all of 2019.
Thanks Mathew for another great post and video. Really interesting to see your exchange with Seheult and good faith attempt to point him towards better statistical reasoning. He is a difficult case since he does make nice content with clear communication and has a lot of hubris. In his Texas tweet, nice to see a few folks pointing out the problems and inconsistencies. He also fails to mention Texas all-cause excess mortality remained positive all through 2022 as seen in the Texas graph at USMortality https://www.usmortality.com/excess-mortality/percentage, and as you have repeatedly explained, we have both HUB and lack of mean reversion.
Side note: I'm happy to relay that I've finally been able to reproduce your correlation time series graph. You briefly showed your Excel table in the video and this reminded me that you are working with daily data. I made scatter plots behind these correlations and see a lot of zeros for daily mortality because a large number of counties only report deaths on a weekly basis. All of these zeros significantly shrink the correlations into the range [-0.1, 0.1]. For what it's worth, I think if the data are aggregated to weekly or monthly, the correlations become much stronger (e.g. -0.5) and the resulting graph becomes more compelling because then there is no doubt we are dealing with significant associations. I'm glad to pitch in on any data analysis tasks you find are stretching your time.
I watched the video above (Episode I: the overconfidence of Dr. Roger Seheult). Maybe you could be more explicit about what you mean by conditional statistics and healthy user bias with simpler examples. The Monty Hall example is too confusing for most people.
Using examples directly related to vaccines, Covid, excess death etc. is a good idea.
Wikipedia does a good job on Healthy User Bias:
"The kind of subjects that take up an intervention, including by enrolling in a clinical trial, are not representative of the general population. People who volunteer for a study can be expected, on average, to be healthier than people who don't volunteer, as they are concerned for their health and are predisposed to follow medical advice,[1] both factors that would aid one's health. In a sense, being healthy or active about one's health is a precondition for becoming a subject of the study, an effect that can appear under other conditions such as studying particular groups of workers. For example, someone in ill health is unlikely to have a job as manual laborer. As a result, studies of manual laborers are studies of people who are currently healthy enough to engage in manual labor, rather than studies of people who would do manual labor if they were healthy enough."
My main point is multiple simple examples are a good idea.
The Simpson's paradox example you brought up might have gone over many peoples heads.
Here's the Wikipedia explanation for those that don't know what it is:
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.