OK, so I finally got time to read this post aside from making snarky Panera comment. So what it appears is that there's far too many confounding variables to really assess the data properly, but unfortunately many people may be deriving far too many conclusions than may be warranted given the information?
I absolutely agree with your statement Brian about "our side". I've become quite dismayed that many people are going down their own rabbit hole of hysteria and unfounded assumptions. I think the Dr. Ardis fiasco just shone a light on everything going on and really makes us look ridiculous to those in the mainstream press.
Unfortunately, this has caused me to react reflexively to posts coming from "our side", and I've become rather quick to disagree with posts that have come out. I think this quick reaction came through in my LNP article yesterday, which after a little bit of stewing I believe jumped the shark on a few topics. I am hoping to either release a correction either today or tomorrow and expand on my ideas.
So, it’s not so much a “look at the confounding” as a back-of-napkin simulation seeking to account for literally all of the weird trends in the ONS data simply by pointing out the vaccines work like “timers,” there were staggered orders of groups approved, and users have to “go up to the counter” to get them. With this analogy/model you explain the weird results even assuming the Panera Kingdom ONS is 100% accurate about whether citizens are holding a timer when they die.
The default used to be emails for any other branch on a thread you engage with, I don’t know if that’s still a thing for some but it stopped for me at some point.
Haha, I had my review sent out the next morning, no reader requests required. But I wrote it from the start intending to be a take-it-or-leave-it outline of the theory + a few notes.
Ironically the coverage by many on Substack may have created a Streisand effect which the media latched onto. I personally chose to cover the Dr. Ardis claims because of Brian so I'll lay some blame on his end- I did so at the end of my first piece.
It is interesting. I would mind if people asked if it was more for our perspective to add to the overall collection of ideas, but I suppose there may be some people who don't make an assessment unless they see other assertions made, which would be a rather concerning thing to consider.
I enjoy your writing style; it's why I'm a paying subscriber.
I disagree that you should write more in a way that the "layman" understands. People who are genuinely interested in data and facts and also understand that "our side" is being played a-plenty by those who purport to be on "our side" are able to discern who is doing the work and who is riding a moment of glory.
One measure I use is to see whose writing has the most "likes" and "comments". If you're someone with hundreds of commenters that means you are not doing the deepest thinking because if you were doing the deepest thinking you wouldn't have so many people subscribed and/or commenting.
I’ll certainly never change my basic style to make it more accessible because I am already trying to be as accessible as possible - within the remit of actually communicating what the research / data says! But usually what “being accessible” entails is abstracting and simplifying the actual story so it’s just a cartoon version. That is what I will not do, as it would require a call to substack support to remove the un from “Unglossed” and I’m too lazy.
May 9, 2022·edited May 9, 2022Liked by Brian Mowrey
It's pretty clear that there are a lot of confounders in this data, and we are limited in what we can do with it. I'm sure ONS could do a much better job, if only the results were to their liking.
However, I had an idea of a way to eliminate a lot of the confounders.
The idea is to consider only 'ever vaxed' and 'never vaxed' as the denominators, and then for each age group plot the time series of deaths for all the various categories of vaxed.
That is, for say 40-50y age group, plot the monthly data by vax status category, using the corresponding month's estimate of the quantity of at least one dose vs. no doses. The various vax category deaths as a stacked line chart, with the denominator of population with at least one dose, compared against the unvaxxed deaths, with the denominator of population never vaxxed.
This captures several things of interest:
1. It eliminates all the worries about why people didn't get the next shot. After the first shot, they are in the vaxxed camp. This way it doesn't matter if they were about to die for whatever reason. That issue remains for those first timers, but really those first timers are pretty tightly clustered in the UK data. The bulk of the vaccinations occurred in the initial rollout - I'm not sure what drove people to get a shot much later than the big rollout, but I'm guessing the decision doesn't have much to do with their health - more likely for administrative reasons, especially in the younger, more healthy groups.
2. It lets us see correlations between booster rollouts and further death, without confounders
3. It captures the cumulative risks in all the shots.
It doesn't address possible biases in the population that meets the various ONS criteria. Perhaps a lot of the vax holdouts are also not in this database. However, by including younger cohorts there should be larger groups of healthy holdouts, which should improve the quality of the data also.
Unfortunately it seems to be a bit of a pain to find the monthy vax numbers broken down by age group (unless you know of a source.. all I know of is the line charts in the UKHSA docs).. so before I embarked on this I thought I would ask if you thought this would be worthwhile...
EDIT: It looks like they provide this info in person-years.
Right, the ONS data can be worked to provide that, and it’s essentially what I recommend at the end, focus on the “real deaths rate” by re-lumping Timers together. Crawford has some plots up for “all vaxxed” in his post today - https://roundingtheearth.substack.com/p/proof-of-statistical-sieves-in-ve - though it’s by age all time rather than by month. But other spreadsheets in the ONS set break down by month.
I agree that “all vaxxed by date” would be where to look for a booster signal, though you get into the problem of waning infection efficacy driving deaths at the same time, this is a separate artifact you could call the “helmets cause soldiers to get shot at” effect. No, helmets just go on more when soldiers are shot at.
But can’t use the by-dose view here either. The Panera Problem assumes absolute accuracy for Timer status. The ONS knows if someone is holding a Timer when they die; and yet, all the same effects occur. This is not a proof that the real ONS has absolute accuracy but I find it compelling enough to not worry about misclassification too much. With that in mind, the Panera ONS data suggests that there “cannot” be a rise in deaths associated with Third Timers, except, subproblem, there actually could and it would be hidden by the not-deathbed-bias.
Sorry, I missed the suggestion at the end of your post!
I'll also check out the other one although I think time series data is important to understand what's going on given all the things that change over time.
I think the only hope of extracting value from this data is to focus on the younger age groups that have some substantial number of holdouts for reasons other than health. Unfortunately the brits are too damned obedient I guess!
Right, that’s why I liked the Israel data once upon a time because you have the Orthodox holdouts as a better control group. However, too much boosting. I believe also that the flaw in the ONS as far as by date data is that there’s still a bad age lumping in the “young” group that totally confounds everything.
Impossible. Unglossed does NOT assert there is an actual problem with Panera, in NO WAY does this post impinge or negatively associate the Panera brand. PANERA DO NOT SOUP I MEAN SUE ME.
The biggest problem is the data is unreliable. We don't really know the real extent of the damage. We know enough to know there is a problem, but no amount of careful analysis of unreliable data can produce reliable results. Obfuscating data is often just incompetence, but sometimes nefarious. Either way, we really need to fix it. That will require a change in governments. It begins in the US in January. If people get the right information, most will make the right decisions. We're in an information war, and the tide is turning.
May 9, 2022·edited May 9, 2022Liked by Brian Mowrey
Brian,
Just a feedback:
Most readers of Substacks are laypeople. And accordingly they gloss, if at all, over any detailed recitation and explanation of data minutia. I do. They just need a few well-written paragraphs and some data in simple tables or charts. Writers can do the detailed analysis "below the line" or as a footnote.
In short, most readers just want, or can only handle, executive summaries.
Thank you. I may edit in a summary at the top. But anything to the effect of “Here is why you can’t trust X’s interpretation of the data!” will just shut readers’ minds, so I would need to be careful there. I’d rather be difficult than didactic, as the latter never changed anyone’s mind about anything.
I write with scanning in mind. The most important parts of this article are directly below the post-Background big headers. Usually I can also move more errata to the footnotes but I’m on Mobile today.
Right, but the meta for Unglossed (reviewed in the Background:Unglossed section of this post) is that the “abstract” version of the science is deceptive and cultish. Would 106% of vaccine-related papers say “X vaccine is safe and effective” if abstracts were banned by death penalty? No. But abstracts open the door for deception and cultish medicine-worship which then gets broadcast to parents via the TV news, and so that is where those things thrive.
So, I am both aversive and cautious with the “if complicated = abstract it = good for lay folk” argument.
Right - I take this to be valid whenever my audience is offering specific feedback, as your comment has done. But in the more general sense, when reporting on science, the outcome of making the data “attractive” is to bamboozle the audience at home.
This has analogs to all forms of writing, including fiction. The easier-to-parse may generate more hits in the short term, but it is less likely to be remembered post-test-of-time. Making the reader do more work results in deeper understanding and impression. “War and Peace” doesn’t lead off with a synopsis, as a heavy-handed example.
May 9, 2022·edited May 9, 2022Liked by Brian Mowrey
There was an scientist, Einstein, who had that famous advice: "simple; but not simpler". Like his E=MC2.
Unless people have an affinity for data and statistics, those two elements are not attractive in any written work. I have not read “War and Peace”; however, I can safely assume it does not contain data and stats.
To me, data and stats are meant for analysis, for textbooks and journals, not lay readers. Also keep in mind that those same people have other articles to read. Most lay-readers do not have a history of reading data and stats.
Readers can go through a Harry Potter book in one sitting; and two for the thicker ones.
Ancient BS Statistics & Computer Science here. The official data is a) wrong, incomplete, confounded, inaccurate, and possibly fudged; b) not intended for analysis, only for headlines.
I find the insurance company exec statements (also cumulative funeral home anecdotes) to be the most interesting, followed by the Walgreens data. Neither dataset claims to be complete in any way, but is likely the most accurate - within their context.
The Walgreens is interesting to ponder the meaning of, but I am not super-happy with their methods description RE some particular point, I think the sequencing setup or something, I’ll go look again.
My previous analysis of (as in exploration of the limits of) the ONS data (linked in “by myself”) ends by pointing out the small absolute counts in the under 40s regardless of status. So I think the exec statements about working age deaths are mostly driven by the middle-aged within that group. I don’t dispute the signal in the younger group but again it tends to only come out to a handful, if looking at 2021 in usmortality or euromomo excess deaths. I think that will begin to change. I’m on team “ticking time-bomb” as far as what post-injection deaths will look like.
May 8, 2022·edited May 8, 2022Liked by Brian Mowrey
Oh lord, at first I had no idea what you were talking about! I was about to search the internet to see if Panera is a country in Europe that I hadn't heard of! 😅😅😅
Okay, the timers are the shots, and timing of shots may be/is statistically significant. Once I got it, the Panera metaphor is actually a creative way to communicate the different statistical difficulties you are pointing out.
Not only is it hard to recognize the metaphor if unfamiliar with Panera, I forgot that Panera buzzers go off when food is ready but not by an actual timer, lol. So it’s a “modified Panera metaphor.” I added a bit to the description of what the timers are to clarify!
Thanks for this - very helpful, and I appreciated the terminology, which eligicted repeated wry smiles: 'UnTimered' and 'Just-First-Timered'... thanks again.
The differences, to put it another way, are because 0 of survivors in any month ever die. So just by saying “come get your next timer” over and over again you keep creating new, temporarily “immortal” <21 day groups especially in the elderly (minus however many the shots kill in that same time frame).
I had the same intuition, but from experience with American corporate culture...
Right, and Kirsch’s choice of comment call-outs RE the “fountain of youth” effect similarly leaves out all the more critical pre-email comments that likely prompted his removal of the original “sanity check” segment, which treated the 1D<21 numbers as a smoking gun, including one of mine. But he’s highlighted my (pre-email) comments in other cases when they are elaborative rather than critical. I find it weird that he never mea culpa’s in general (why not? It’s free credibility points), and I think his employment of the “pre-email peer review posting” has made him a little less scrupulous about acknowledging post-email edits.
Moving time windows are very difficult to work with if one is not prepared to go home empty handed after investing the work. Kirsch could probably easily avoid these mistakes (also committed in his 2021 82% miscarriage claim and CDC uncategorized deaths claim) if he would just take a more open minded approach to the data.
Sorting by Chronological will get you straight to the pre-email comments. To Kirsch’s credit, he rarely pay-gates commenting and yet engages heavily, putting a lot more work on himself (but also leading to spam-bloat).
Off topic, but I just wanted somewhere to vent about this latest survey and results Kirsch just put up. I tried to be respectful in my reply but it’s incredibly irritating to see the counter-narrative folks completely jump the shark here. Either of you see this garbage yet? 1500 self-reporting responses from a hopelessly biased audience, of which over half the responses have different questions asked! Yet this is a “big development.” It’s infuriating and I’m starting to feel like there’s almost no one left with any common sense.
“Like you, I only really care about what is true” - Well, there’s your first mistake. Most don’t care about that at all or the few that do ultimately throw that away once they have a narrative that others are buying. After all, you can’t have a brand if you’re constantly changing the ingredients in your secret sauce.
Or should I say “soup” since I’m not sure Panera has a secret sauce? Probably not because trying to tie my comment back to the article could be more confusing than the timer analogy.
What would Kirsch need that for, though? He had very profitable businesses (plural) going, that he left, to not damage them by his reputation (at least that's the reason he's given), exposing himself so much as "against The Science(TM)".
As for selling a product, this must be a mighty downgrade compared to his previous entreprenourship. In other words, I doubt that's his driving motivation.
I forgot to reply to this, and it probably doesn’t matter anymore, but to folks like Kirsch, building a brand I suspect is basically like breathing. He’s had a life of following certain practices to be successful and he’s not going to change now. His heart is in the right place, but like many, he’s a true believer… just one with a contrarian streak.
OK, so I finally got time to read this post aside from making snarky Panera comment. So what it appears is that there's far too many confounding variables to really assess the data properly, but unfortunately many people may be deriving far too many conclusions than may be warranted given the information?
I absolutely agree with your statement Brian about "our side". I've become quite dismayed that many people are going down their own rabbit hole of hysteria and unfounded assumptions. I think the Dr. Ardis fiasco just shone a light on everything going on and really makes us look ridiculous to those in the mainstream press.
Unfortunately, this has caused me to react reflexively to posts coming from "our side", and I've become rather quick to disagree with posts that have come out. I think this quick reaction came through in my LNP article yesterday, which after a little bit of stewing I believe jumped the shark on a few topics. I am hoping to either release a correction either today or tomorrow and expand on my ideas.
So, it’s not so much a “look at the confounding” as a back-of-napkin simulation seeking to account for literally all of the weird trends in the ONS data simply by pointing out the vaccines work like “timers,” there were staggered orders of groups approved, and users have to “go up to the counter” to get them. With this analogy/model you explain the weird results even assuming the Panera Kingdom ONS is 100% accurate about whether citizens are holding a timer when they die.
The default used to be emails for any other branch on a thread you engage with, I don’t know if that’s still a thing for some but it stopped for me at some point.
Haha, I had my review sent out the next morning, no reader requests required. But I wrote it from the start intending to be a take-it-or-leave-it outline of the theory + a few notes.
Ironically the coverage by many on Substack may have created a Streisand effect which the media latched onto. I personally chose to cover the Dr. Ardis claims because of Brian so I'll lay some blame on his end- I did so at the end of my first piece.
It is interesting. I would mind if people asked if it was more for our perspective to add to the overall collection of ideas, but I suppose there may be some people who don't make an assessment unless they see other assertions made, which would be a rather concerning thing to consider.
I enjoy your writing style; it's why I'm a paying subscriber.
I disagree that you should write more in a way that the "layman" understands. People who are genuinely interested in data and facts and also understand that "our side" is being played a-plenty by those who purport to be on "our side" are able to discern who is doing the work and who is riding a moment of glory.
One measure I use is to see whose writing has the most "likes" and "comments". If you're someone with hundreds of commenters that means you are not doing the deepest thinking because if you were doing the deepest thinking you wouldn't have so many people subscribed and/or commenting.
Thank you, as always!
I’ll certainly never change my basic style to make it more accessible because I am already trying to be as accessible as possible - within the remit of actually communicating what the research / data says! But usually what “being accessible” entails is abstracting and simplifying the actual story so it’s just a cartoon version. That is what I will not do, as it would require a call to substack support to remove the un from “Unglossed” and I’m too lazy.
I've never been to a Panera.
It is a life-changing, and indescribable experience. Akin to seeing God emerge in the form of a buzzing coaster.
😄 There's one about a mile from me; I guess I'll need to give it a try.
It's pretty clear that there are a lot of confounders in this data, and we are limited in what we can do with it. I'm sure ONS could do a much better job, if only the results were to their liking.
However, I had an idea of a way to eliminate a lot of the confounders.
The idea is to consider only 'ever vaxed' and 'never vaxed' as the denominators, and then for each age group plot the time series of deaths for all the various categories of vaxed.
That is, for say 40-50y age group, plot the monthly data by vax status category, using the corresponding month's estimate of the quantity of at least one dose vs. no doses. The various vax category deaths as a stacked line chart, with the denominator of population with at least one dose, compared against the unvaxxed deaths, with the denominator of population never vaxxed.
This captures several things of interest:
1. It eliminates all the worries about why people didn't get the next shot. After the first shot, they are in the vaxxed camp. This way it doesn't matter if they were about to die for whatever reason. That issue remains for those first timers, but really those first timers are pretty tightly clustered in the UK data. The bulk of the vaccinations occurred in the initial rollout - I'm not sure what drove people to get a shot much later than the big rollout, but I'm guessing the decision doesn't have much to do with their health - more likely for administrative reasons, especially in the younger, more healthy groups.
2. It lets us see correlations between booster rollouts and further death, without confounders
3. It captures the cumulative risks in all the shots.
It doesn't address possible biases in the population that meets the various ONS criteria. Perhaps a lot of the vax holdouts are also not in this database. However, by including younger cohorts there should be larger groups of healthy holdouts, which should improve the quality of the data also.
Unfortunately it seems to be a bit of a pain to find the monthy vax numbers broken down by age group (unless you know of a source.. all I know of is the line charts in the UKHSA docs).. so before I embarked on this I thought I would ask if you thought this would be worthwhile...
EDIT: It looks like they provide this info in person-years.
Right, the ONS data can be worked to provide that, and it’s essentially what I recommend at the end, focus on the “real deaths rate” by re-lumping Timers together. Crawford has some plots up for “all vaxxed” in his post today - https://roundingtheearth.substack.com/p/proof-of-statistical-sieves-in-ve - though it’s by age all time rather than by month. But other spreadsheets in the ONS set break down by month.
I agree that “all vaxxed by date” would be where to look for a booster signal, though you get into the problem of waning infection efficacy driving deaths at the same time, this is a separate artifact you could call the “helmets cause soldiers to get shot at” effect. No, helmets just go on more when soldiers are shot at.
But can’t use the by-dose view here either. The Panera Problem assumes absolute accuracy for Timer status. The ONS knows if someone is holding a Timer when they die; and yet, all the same effects occur. This is not a proof that the real ONS has absolute accuracy but I find it compelling enough to not worry about misclassification too much. With that in mind, the Panera ONS data suggests that there “cannot” be a rise in deaths associated with Third Timers, except, subproblem, there actually could and it would be hidden by the not-deathbed-bias.
Sorry, I missed the suggestion at the end of your post!
I'll also check out the other one although I think time series data is important to understand what's going on given all the things that change over time.
I think the only hope of extracting value from this data is to focus on the younger age groups that have some substantial number of holdouts for reasons other than health. Unfortunately the brits are too damned obedient I guess!
Right, that’s why I liked the Israel data once upon a time because you have the Orthodox holdouts as a better control group. However, too much boosting. I believe also that the flaw in the ONS as far as by date data is that there’s still a bad age lumping in the “young” group that totally confounds everything.
Yes, the lumping together under 39yrs is highly suspect also.
Not going to lie, seeing everyone post about the "Panera problem" I thought we were going to find out another soup was not made inhouse!
Impossible. Unglossed does NOT assert there is an actual problem with Panera, in NO WAY does this post impinge or negatively associate the Panera brand. PANERA DO NOT SOUP I MEAN SUE ME.
The biggest problem is the data is unreliable. We don't really know the real extent of the damage. We know enough to know there is a problem, but no amount of careful analysis of unreliable data can produce reliable results. Obfuscating data is often just incompetence, but sometimes nefarious. Either way, we really need to fix it. That will require a change in governments. It begins in the US in January. If people get the right information, most will make the right decisions. We're in an information war, and the tide is turning.
Brian,
Just a feedback:
Most readers of Substacks are laypeople. And accordingly they gloss, if at all, over any detailed recitation and explanation of data minutia. I do. They just need a few well-written paragraphs and some data in simple tables or charts. Writers can do the detailed analysis "below the line" or as a footnote.
In short, most readers just want, or can only handle, executive summaries.
I did a quick scan of this article.
Thank you. I may edit in a summary at the top. But anything to the effect of “Here is why you can’t trust X’s interpretation of the data!” will just shut readers’ minds, so I would need to be careful there. I’d rather be difficult than didactic, as the latter never changed anyone’s mind about anything.
I write with scanning in mind. The most important parts of this article are directly below the post-Background big headers. Usually I can also move more errata to the footnotes but I’m on Mobile today.
In order to be informing, a reader must first read it, understand it, and assimilate it. If people don't read....
There is a reason why people find textbooks boring. Textbooks; not even journals. Bart Simpson left his in its plastic wrapper...
Do what the pros do: have an abstract for any long article. However, make sure the abstract aligns with the data and analysis...
Right, but the meta for Unglossed (reviewed in the Background:Unglossed section of this post) is that the “abstract” version of the science is deceptive and cultish. Would 106% of vaccine-related papers say “X vaccine is safe and effective” if abstracts were banned by death penalty? No. But abstracts open the door for deception and cultish medicine-worship which then gets broadcast to parents via the TV news, and so that is where those things thrive.
So, I am both aversive and cautious with the “if complicated = abstract it = good for lay folk” argument.
I can see your view.
For any writer, here is the only question that matters: how do I make my writing attractive to my audience?
Right - I take this to be valid whenever my audience is offering specific feedback, as your comment has done. But in the more general sense, when reporting on science, the outcome of making the data “attractive” is to bamboozle the audience at home.
This has analogs to all forms of writing, including fiction. The easier-to-parse may generate more hits in the short term, but it is less likely to be remembered post-test-of-time. Making the reader do more work results in deeper understanding and impression. “War and Peace” doesn’t lead off with a synopsis, as a heavy-handed example.
There was an scientist, Einstein, who had that famous advice: "simple; but not simpler". Like his E=MC2.
Unless people have an affinity for data and statistics, those two elements are not attractive in any written work. I have not read “War and Peace”; however, I can safely assume it does not contain data and stats.
To me, data and stats are meant for analysis, for textbooks and journals, not lay readers. Also keep in mind that those same people have other articles to read. Most lay-readers do not have a history of reading data and stats.
Readers can go through a Harry Potter book in one sitting; and two for the thicker ones.
Ancient BS Statistics & Computer Science here. The official data is a) wrong, incomplete, confounded, inaccurate, and possibly fudged; b) not intended for analysis, only for headlines.
I find the insurance company exec statements (also cumulative funeral home anecdotes) to be the most interesting, followed by the Walgreens data. Neither dataset claims to be complete in any way, but is likely the most accurate - within their context.
The Walgreens is interesting to ponder the meaning of, but I am not super-happy with their methods description RE some particular point, I think the sequencing setup or something, I’ll go look again.
My previous analysis of (as in exploration of the limits of) the ONS data (linked in “by myself”) ends by pointing out the small absolute counts in the under 40s regardless of status. So I think the exec statements about working age deaths are mostly driven by the middle-aged within that group. I don’t dispute the signal in the younger group but again it tends to only come out to a handful, if looking at 2021 in usmortality or euromomo excess deaths. I think that will begin to change. I’m on team “ticking time-bomb” as far as what post-injection deaths will look like.
Oh lord, at first I had no idea what you were talking about! I was about to search the internet to see if Panera is a country in Europe that I hadn't heard of! 😅😅😅
Okay, the timers are the shots, and timing of shots may be/is statistically significant. Once I got it, the Panera metaphor is actually a creative way to communicate the different statistical difficulties you are pointing out.
"I was about to search the internet to see if Panera is a country in Europe that I hadn't heard of!"
Same here!
😄👍🏼
Not only is it hard to recognize the metaphor if unfamiliar with Panera, I forgot that Panera buzzers go off when food is ready but not by an actual timer, lol. So it’s a “modified Panera metaphor.” I added a bit to the description of what the timers are to clarify!
Thanks for this - very helpful, and I appreciated the terminology, which eligicted repeated wry smiles: 'UnTimered' and 'Just-First-Timered'... thanks again.
Would help if I could spell elicited!
I hadn’t even noticed - it is both easy to make and not notice typos in the small-by-default substack comments UI!
The differences, to put it another way, are because 0 of survivors in any month ever die. So just by saying “come get your next timer” over and over again you keep creating new, temporarily “immortal” <21 day groups especially in the elderly (minus however many the shots kill in that same time frame).
I had the same intuition, but from experience with American corporate culture...
Right, and Kirsch’s choice of comment call-outs RE the “fountain of youth” effect similarly leaves out all the more critical pre-email comments that likely prompted his removal of the original “sanity check” segment, which treated the 1D<21 numbers as a smoking gun, including one of mine. But he’s highlighted my (pre-email) comments in other cases when they are elaborative rather than critical. I find it weird that he never mea culpa’s in general (why not? It’s free credibility points), and I think his employment of the “pre-email peer review posting” has made him a little less scrupulous about acknowledging post-email edits.
Moving time windows are very difficult to work with if one is not prepared to go home empty handed after investing the work. Kirsch could probably easily avoid these mistakes (also committed in his 2021 82% miscarriage claim and CDC uncategorized deaths claim) if he would just take a more open minded approach to the data.
“why not? It’s free credibility points” - missed opportunity to faux-ironically bemoan how rarely I make mistakes, lol
Sorting by Chronological will get you straight to the pre-email comments. To Kirsch’s credit, he rarely pay-gates commenting and yet engages heavily, putting a lot more work on himself (but also leading to spam-bloat).
Off topic, but I just wanted somewhere to vent about this latest survey and results Kirsch just put up. I tried to be respectful in my reply but it’s incredibly irritating to see the counter-narrative folks completely jump the shark here. Either of you see this garbage yet? 1500 self-reporting responses from a hopelessly biased audience, of which over half the responses have different questions asked! Yet this is a “big development.” It’s infuriating and I’m starting to feel like there’s almost no one left with any common sense.
Ok, good burn there.
“Like you, I only really care about what is true” - Well, there’s your first mistake. Most don’t care about that at all or the few that do ultimately throw that away once they have a narrative that others are buying. After all, you can’t have a brand if you’re constantly changing the ingredients in your secret sauce.
Or should I say “soup” since I’m not sure Panera has a secret sauce? Probably not because trying to tie my comment back to the article could be more confusing than the timer analogy.
What would Kirsch need that for, though? He had very profitable businesses (plural) going, that he left, to not damage them by his reputation (at least that's the reason he's given), exposing himself so much as "against The Science(TM)".
As for selling a product, this must be a mighty downgrade compared to his previous entreprenourship. In other words, I doubt that's his driving motivation.
I forgot to reply to this, and it probably doesn’t matter anymore, but to folks like Kirsch, building a brand I suspect is basically like breathing. He’s had a life of following certain practices to be successful and he’s not going to change now. His heart is in the right place, but like many, he’s a true believer… just one with a contrarian streak.