Er verschijnen met grote regelmaat nieuwe studies die claimen dat COVID-19-vaccinaties een "beschermend effect" hebben tegen oversterfte. Ook de nieuwste publicatie in European Journal of Epidemiology (2026)1The publication discussed: https://link.springer.com/article/10.1007/s10654-026-01414-1 volgt weer hetzelfde platgetreden pad. En ik ben bang dat er nog vele gaan volgen. De auteurs presenteren deze keer een complex "multi-state model", maar in hun eigen limitations and methodological choices, it can be read that the conclusions of this study do not make sense. Remarkable methodology is also described in the appendix. In any case, we can add this study to the list of studies in which deficiencies are mentioned but not addressed. They are simply calculated as if they did not exist.
The authors should have thrown in the towel as soon as they noticed the poor data quality. But yes, the subsidy was received, what do you do? Then you simply reason through to the desired conclusions and casually make it clear in the text that the data were insufficient.
So: here we go again. (That money laundering PEC is uninteresting. A viewing tip: Rob Elens on June 17.)
1. Het "Healthy Vaccinee Effect" voor de zóveelste keer als blinde vlek
The authors report that their comparison between vaccinated and unvaccinated people is fundamentally distorted by it Healthy Vaccinee Effect (HVE):
"The healthy vaccinee bias likely also leads to some overestimation of the differences between the excess mortality risks from the (non-)recent infection states with and without prior vaccination."
"De vertekening door de gezonde gevaccineerden leidt waarschijnlijk ook tot enige overschatting van de verschillen tussen de oversterfterisico's bij (niet-)recente infecties met en zonder voorafgaande vaccinatie."
Met de term "enige overschatting" bagatelliseren ze een enorm probleem. Het is een understatement van jewelste. HVE betekent dat gezonde mensen zich laten vaccineren, terwijl de meest kwetsbaren (van wie vaak het levenseinde in zicht is) die stap niet meer maken of kunnen maken. Als je vervolgens een lagere sterfte ziet in de gevaccineerde groep, mis je dus die mensen. Je meet het effect van "gezond zijn" en "geen overlijden te verwachten", niet het effect van de injectie. Eigenlijk zie je de verhoogde sterfte in de groep ongevaccineerden, waarin immers mensen zijn ondergebracht die op korte termijn zouden gaan overlijden. De auteurs erkennen dit probleem in de limitations, but nevertheless simply use the results 1:1 in their conclusion to claim that vaccination protects.
2. De "ongevaccineerde" groep is fictie
De definitie van wie "ongevaccineerd" is staat ook in dit onderzoek op losse schroeven.
"about 8–25% of those considered as the unvaccinated group in our dataset were actually vaccinated"
"Ongeveer 8-25% van degenen die in onze dataset als niet-gevaccineerd werden beschouwd, waren in werkelijkheid wel gevaccineerd."
A misclassification of this order of magnitude is fatal for any epidemiological study. It is more than 22% among those under 90! (That 8% only applies to the very small group of people over 90, who later turn out to have been inflated as well, as we see in the appendices).
| Birthyear cohort | % vaccinated as reported by RIVM 5 | % vaccinated based on our data (B) | % of vaccinated according to RIVM is recorded in our data (C=B/A (%)) | % missed (D=A-C) | % not vaccinated according to RIVM (100-A) | % not vaccinated according to our data (E=100-B) | % misclassified among the ‘not vaccinated’ in our data (D/E (%)) |
|---|---|---|---|---|---|---|---|
| <1931 | 89 | 88 | 99 | 1 | 11 | 12 | 8% |
| 1931-1935 | 93 | 91 | 98 | 2 | 7 | 9 | 22% |
| 1936-1940 | 94 | 92 | 98 | 2 | 6 | 8 | 25% |
| 1941-1945 | 94 | 93 | 99 | 1 | 6 | 7 | 14% |
| 1946-1950 | 94 | 92 | 98 | 2 | 6 | 8 | 25% |
| 1951-1955 | 93 | 91 | 98 | 2 | 7 | 9 | 22% |
De auteurs geven aan dat in een aantal verpleeghuizen de data ontbrak. Die bewoners zijn simpelweg als "ongevaccineerd" gelabeld. En daar stierven mensen, ook binnen twee weken na vaccinatie, de voorbeelden zijn bekend ("We waren nét te laat met vaccineren!") . Dit versterkt de bias: de ongevaccineerde groep bevat hierdoor disproportioneel veel hoog-risico patiënten.
In fact, this is already the final blow to the reliability of the study. The 'unvaccinated' group has become an administrative dumping ground for the most vulnerable patients, while the 'vaccinated' group contains the healthy survivors. A comparison between these two groups therefore says nothing at all about the effectiveness of the vaccine, but everything about the flaws in the registration.
3. A sensitivity analysis with a different time frame
In a sensitivity analysis, they only show the status of vaccinated people 14 days after the injection. They present this as a responsible check for building immunity:
"As it takes some time to build up immunity after vaccination against SARS-CoV-2, we performed a sensitivity analysis with people changing their vaccination status 2 weeks after their actual vaccination date"
"Omdat het enige tijd duurt om immuniteit op te bouwen na vaccinatie tegen SARS-CoV-2, hebben we een gevoeligheidsanalyse uitgevoerd waarbij mensen hun vaccinatiestatus wijzigden 2 weken na hun werkelijke vaccinatiedatum."
So they cut two weeks out of their observed period and then arrive at comparable results. In those two weeks, we know from Bakker's Kaplan-Meier graphs and from Italy, many people die who have no longer been vaccinated because they would die. Yet the results remain robust and comparable, without further explanation as to how this is possible.
Met het overslaan van die 14 dagen creëert de studie een groep "gevaccineerden" die bestaat uit mensen die de eerste twee weken hebben overleefd. De slachtoffers van die eerste periode worden bij de ongevaccineerden opgeteld, waardoor de gevaccineerde groep kunstmatig vele malen "gezonder" oogt dan de werkelijkheid. Vele malen, omdat een relatief klein aantal dat zo uit de grote groep gevaccineerden wordt weggefilterd, een substantieel deel uitmaakt van de veel kleinere groep ongevaccineerden. Deze ingreep was kennelijk nodig om de gevoeligheidsanalyse in lijn te brengen met de vervuilde data in de hoofdstudie. In studies naar vaccin-effectiviteit gebeurt dit vaak, omdat eventuele schade in de eerste twee weken maar zou afleiden van de bescherming tegen de ziekte in kwestie.
4. Under-reporting of natural immunity
The model rests on a shaky foundation of documented infections:
"the cumulative incidence of infection in our cohort was only 9% at the end of 2021, showing that we missed quite some undocumented infections."
"De cumulatieve incidentie van infectie in onze cohort bedroeg eind 2021 slechts 9%, wat aantoont dat we een aanzienlijk aantal niet-gedocumenteerde infecties over het hoofd hebben gezien."
De werkelijke seroprevalentie lag rond de 26%. Dat betekent dat miljoenen mensen natuurlijke immuniteit hadden opgebouwd zonder dat dit in de data van de onderzoekers terechtkwam. Deze mensen worden in het model als "event-free" of "ongevaccineerd zonder infectie" behandeld. Hierdoor wordt de bescherming van natuurlijke immuniteit genegeerd of toegeschreven aan de vaccinatie-status, wat de effectiviteit van de prik verder vertroebelt.
5. De "negatieve oversterftekans": bewijs van bias
The authors report this remarkable result:
"For all subsets, vaccinated persons without any documented infection had a negative excess death probability at the end of follow-up."
"Voor alle subgroepen hadden gevaccineerde personen zonder gedocumenteerde infectie een negatieve oversterftekans aan het einde van de follow-up."
Een "negatieve oversterfte" betekent dat er minder mensen stierven dan statistisch verwacht, hier zelfs specifiek de mensen die niet eens Covid hadden. In de context van een pandemie is dit biologisch onwaarschijnlijk voor een bevolkingsgroep. Dit is het ultieme bewijs van de healthy vaccine bias: de groep "gevaccineerde personen zonder infectie" blijkt in de subsets de gezondste van allemaal. Het model schrijft dit "wonder" toe aan de vaccinatie, terwijl het een puur statistisch artefact is van het selectie-effect.
On page 3 of the appendix is the confession that should actually be enough to retract the entire paper:
"This may lead to negative excess hazards of death... possibly even below zero... This phenomenon is more often observed when relative survival is combined with a multi-state model, since the persons’ fitness may influence which states they reach... This likely also occurs in our model, where excess mortality from the vaccination state was negative, probably mainly caused by the healthy vaccinee effect."
"Dit kan leiden tot negatieve oversterfterisico's… mogelijk zelfs onder nul… Dit fenomeen wordt vaker waargenomen wanneer relatieve overleving wordt gecombineerd met een model met meerdere toestanden, aangezien de fitheid van personen van invloed kan zijn op welke toestanden ze bereiken… Dit gebeurt waarschijnlijk ook in ons model, waar de oversterfte vanuit de vaccinatietoestand negatief was, waarschijnlijk voornamelijk veroorzaakt door het Healthy Vaccinee Effect."
Dus hun model vindt "negatieve sterfte" (mensen die minder sterven dan biologisch mogelijk is volgens het model) zodra je naar de gevaccineerden kijkt. Ze benoemen dit als een gevolg van het Healthy Vaccinee Effect. So they know that their model impossibly low mortality rates berekent voor gevaccineerden, maar in plaats van te concluderen dat het model niet deugt, noemen ze het een "fenomeen" en gaan ze door met de analyse en alle andere waarden die uit het model komen.
Professional statisticians may find this the most normal thing in the world. But what I see: if they judge something as wrong, it is simply a recognized problem with the chosen model - and thus solved. Where they recognize no error and where their theory is confirmed, the model has done its job well. What a coincidence!
6. De "gekloonde" ouderen
Dit is technisch gezien misschien wel het meest bizarre punt. Om voor de groep 90-plussers genoeg data te krijgen, hebben ze mensen "gekloond":
"For the ages >90 years, some persons were cloned in the dataset: e.g., for age 91, all persons aged 89, 90, 92 and 93 were added again with an artificial age of 91, creating a 5-year age group centered around the age of interest."
"Voor de leeftijden boven de 90 jaar zijn sommige personen in de dataset gekloond: bijvoorbeeld, voor de leeftijd van 91 jaar zijn alle personen van 89, 90, 92 en 93 jaar opnieuw toegevoegd met een kunstmatige leeftijd van 91, waardoor een leeftijdsgroep van 5 jaar is ontstaan rond de leeftijd waarin we geïnteresseerd zijn."
Ze hebben mensen kunstmatig in het model gezet om de groepen groter te laten lijken. Ze hebben een 89-jarige of een 93-jarige "gekopieerd" en hem het label "91-jarige" gegeven. Dit is data-fabricage onder het mom van "smoothing". Dit vertekent de mortaliteitsrisico's voor de oudste (en meest kwetsbare) groepen volledig. Dit toont hoe je de data kunt "repareren" om het model werkend te krijgen...
Conclusion
Deze studie is een schoolvoorbeeld van hoe "geavanceerde methodologieën" (zoals multi-state modellen) kunnen worden gebruikt om een vooraf gewenst resultaat te produceren. Statistiek blijkt steeds meer een goocheldoos. Door de zwaktes diep in de tekst te verstoppen, kunnen de auteurs een conclusie trekken die voor beleidsmakers en media als "veilig" wordt beoordeeld, terwijl de data zelf aantonen dat de vergelijking tussen de groepen fundamenteel mank gaat.
Wetenschap is niet het publiceren van modellen die de werkelijkheid "passend" rekenen. Wetenschap zou moeten erkennen dat als je slordig groepen vergelijkt die fundamenteel van elkaar verschillen (de gezonde gevaccineerde vs. de zieke ongevaccineerde), je geen conclusies kunt trekken over de veiligheid van een medische interventie. Dit rapport bevestigt het herhaaldelijk geleverde bewijs dat we moeten stoppen met het accepteren van "statistische correcties" als definitieve vervanging voor absolute sterftecijfers.
Don't be fooled by a complex 'multi-state model' and mathematical feats. This model is simply a new camouflage net over the same battlefield. More subsets and more complicated variables seem to capture reality, but such a model only works if your data is razor-sharp. If not, you construct a model reality.
At the same time, it makes me wonder why they hand out those weaknesses on a tray. They do a magic trick and immediately explain how it works. If you really want to mess with your eyes, that's not a good thing to do. Or would they really underestimate the effect of those weaknesses? I think the latter, the result of a deep-seated bias. Play with the calculator below, understand how it calculates, and then you'll see for yourself.
The power of misclassification
How drastic apparently small misclassifications are becomes clear when we measure a placebo. An injection that has no effect, but where we also cannot clearly distinguish between injected and not injected. Because this pollution leads to a positive effect, we interpret it as the effectiveness of the injection.
See the calculator below.
In the slider you indicate the percentage of people who will die in the coming months for which you see the end coming in advance. Consider a period of months; then a double vaccination with a long exposure time is no longer useful. Add to this the people who are not eligible for an injection (or who do not want to) because they are simply too weak or too ill to properly process an injection.
If the total is around 10% of people, the placebo effectiveness is 69%.
If the total is around 30% of people, then the placebo effectiveness is 90%.
That's how fast it goes. That's simple math and certainly not a medical miracle.
Footnotes
- 1The publication discussed: https://link.springer.com/article/10.1007/s10654-026-01414-1
It's that time again...the next fairy tale from the big fairy tale book.
Do you know Dr Clare Craig's books?
1, Expired, about the myths during the pandemic
2. Spiked, on the effectiveness of the vaccines
She spoke about Spiked with Dr Campbell on YouTube
Both books very highly recommended.
Available at Amazon.nl
This is really crazy for words... The total bankruptcy of universities and scientists.
Is it feasible/wise to file a complaint with the CWI of the LUMC on the basis of the incorrect basic data mentioned, which is nevertheless processed into a favorable result for the financier.
Another employee (Alexander Gorbalenya) of the LUMC was also involved in publishing unreliable science regarding the corona outbreak.
A note to eur j epidemiol perhaps? It is not said that authors make such mistakes on purpose.
Indeed, a publication in the form of a so-called “letter to the editor” based on The above analysis including calculation example would be a very powerful and valuable signal.
“The wish of the thought and/or financier” Resveratrol was in the news years ago and this elixir of life is found in red wine, among other things. The myth that drinking red wine is healthy is still alive among wine lovers. It was a party when people heard about the enormously healthy effect of drinking wine every day 🥂 It is an open secret that one must drink 7 - 10 liters of wine per day to obtain the required daily amount of resveratrol. People don't want to hear that and close themselves off from knowing. This amount of wine per day is deadly, but everyone could understand that or not, because with corona almost no one wants to hear/know about the errors in studies. Peter van der Voort (ICU doctor Groningen and from D66 has conducted research (or is still conducting research 🤷) into resveratrol (obviously not based on wine) in (obese) corona patients. I will look up this research again, but his voting behavior in the first chamber was so contradictory to his good intentions that for me it was yet another proof that people were perhaps being controlled by intelligence services 🤷 The level of science has been declining for a long time anyway...
Submitting a protest or complaint to assess the ethical scientific behavior of the authors of this publication is made more difficult because a co-author of this misleading publication is a member of the Editorial Board of the journal “European Journal of Epidemiology”. The same person is also chairman of the CWI of Leiden University and the LUMC and plays a key role in other organizations in the field of scientific integrity.
Willem knows who it is about…
Nice Anton, I had planned to delve into it again and look for the weaknesses... but that is no longer necessary. 😀
In addition, the statistical predictive power of a 2015-2019 baseline mortality is not very useful.
So it can go straight into the trash.
I also haven't yet understood exactly how they extrapolated the forecast. You can use 2015-2019 as a basis in many ways, of course; also the stratified versions.
Anton, what a beautiful sentence you wrote again: 'a new camouflage net over the same battlefield'. I hope you find some joy in writing your beautiful and razor-sharp lyrics. Because the abuses described would make you completely hopeless. At least I do. And I admire people (like you) who continue to point out the many sore spots. And 'sore spots' is putting it too mildly, they are deep smelly wounds.
What do you think of it: Healthy queuing effect in the Netherlands 😉
https://www.ad.nl/binnenland/verbazing-over-rij-hoogbejaarden-in-regen-en-kou-voor-prik-niet-de-bedoeling~a15de156/
I don't really feel like responding in detail to this study. Still feel the urge to say something. As many here know, I was trained at the LUMC at a time when the epidemiologists there were very strict about looking at cohort studies that included participants who had been using a drug for a while before that observation period was started. Studies that did that, the authors of which often did so out of convenience and stupidity, we argued, but it was stupid!
Harmful too.
The most striking example that was almost always cited to teach about the above problem (that which is also called 'prevalent user bias'), were observational studies from the 1980s in which women who were going through menopause used so-called HRT (hormone replacement therapy) to mask/mitigate their menopausal symptoms. These studies showed that these women had a LOWER risk of developing cardiovascular disease than women who did not use HRT. The then internationally applicable (American) guideline for cardiologists suggested that HRT should be given to all women who were going through menopause, because it protected against CVD.
18 years later the redeeming word finally came from a randomized study and it turned out: HRT gave a HIGHER risk of cardiovascular disease.
What had happened? – The cohort studies that found a lower risk of CVD did not look at how long women had been using HRT before entering the study (some had been using it for months to years before the observation period started). And while the risk of CVD (the trial showed) with HRT use was mainly in the initial period of intake. A form of bias, not very different from what the authors show in eur j epidemiol, where not all participants were followed from the moment of the first injection, and where (it seems, see articles by Herman) the risk of sudden death increases shortly after each new injection (round).
In a piece from 2018 I summarized everything and said (with the department where I worked at the time):
‘The lesson learned from the HRT controversy [maar dus ook in vaccinstudies] is that in observational studies one should not forget those who did not survive or developed the disease outcome of interest before the study started including participants.’
This did happen in the above-mentioned study and it is all the more sad for yours truly to note that the intellectual level of his alma mater has apparently forgotten a lesson that it itself taught students with great verve for years.
What does that have to do with? Don't know. What certainly didn't help is all the jargon in the article, half of which I don't understand, and which I think may have confused the authors themselves. Not everything can be solved with a model.
See: https://pmc.ncbi.nlm.nih.gov/articles/PMC6332773/
Doesn't surprise me. Of course, HVE is a very old known phenomenon. But that's sad. just when it really matters. those lessons have been completely forgotten. And it even seems like “deliberately forgetting”.
Or those people at the universities are very stupid; Which wouldn't surprise me either. The average VWO student can no longer even solve 2 equations with 2 unknowns... Then understanding the HVE is really too complicated.
Off topic, Tamara van de Ark, but very interesting for the readers here...
It is very interesting and crucial and morally revealing what Tamara vd Ark says here. https://www.youtube.com/watch?v=ob6MSzWtfpA She explicitly says that 10 patients here and now are more important than 100 tomorrow and the day after. She even refers to the RIVM report from early July 2022, which reports that 320,000 healthy years of life have been lost due to isolation measures and the scaling down of regular care. And that she still has to deal with it in her current position. Of course, she fails to draw the right conclusion from this. But she can't; that is not ill will or intent or loss of memory.
It is precisely the mechanism of the Rule of Rescue vs. utilitarianism. See Orr & Wolff. Apart from the fact that she built the policy on incorrect models from RIVM [for which, in my opinion, she is not much to blame], she tells a completely honest and logical story. But so morally reprehensible; but almost everyone (more than 90%) thought so; and still thinks that way. And the Commission does not realize this: the devastating effect of the RoR if it is applied on a macro scale. Only Eline vd Broek really understands this. Unfortunately, Ira Helsloot and Maurice + Marianne still don't seem to understand this either. And if this concept does not land, it will be exactly like this next time: short-term health interests literally weigh 10 to 100 times more than long-term interests. That is why the qaly standard has also been exceeded by a factor of 75 due to the lockdown (20,000/qaly): 150 billion/100,000 qaly is 1.5 million/qaly.
So I think that here too there is someone who, in complete good faith, infected by the inherently positive Rule of Rescue thinking, has made completely wrong decisions on a macro scale. Telling her that she is “bad through and through” and suffers from amnesia misses the point. The whole of the Netherlands and the whole of Europe were under the spell of the RoR! And still! And almost no one realizes it!
See: https://www.lighthousetv.nl/uitzending/lhtv-87
And as a joke: https://www.youtube.com/watch?v=Ft5E5Fh5esU&t=96s
The sacred IC 'corona' patient
I wonder if following the Rule of Rescue is not partly inspired by the Rule of Rutte.
Rutte, as Roskam Abbing reported in his interrogation, drew a red line that meant that no Covid patient should suffocate because there would be no room in the ICU. We did not let them suffocate, abandon them.
This seems to conflict with the policy of sailing closer to the wind in terms of IC occupancy over the course of the Rutte years. (with the approval of, for example, Head of IC Gommers: otherwise they would just get bored)
You then run the risk of having to say no at some point.
The fact that things can be done differently is illustrated by our neighboring country Germany, which with good IC capacity did not experience excess mortality in the first wave.
(Aren't German ICU nurses bored, I wonder, or is it organized differently there?)
Placing vulnerable people at all costs and keeping them alive through (presumably inadequate) intubation etc. in an ICU, thus further limiting the already limited ICU capacity, has not done our country any good.
I suspect (see also Interview with Marianne) that someone like Armand Gisbers advocates good circulation in the ICU: he wanted to limit the length of time spent in the ICU and thus avoid the ICU infarction. Was the Rule of Rutte canonized?
The Corona pts in the ICU also seem to have been given a kind of sacred status: with all their comorbidities, they were not only allowed to die there, this death also had to be postponed for as long as possible with all kinds of tricks and tricks.
I think I see the same pattern here as in the course of events at MH 17.
Instead of asking whether something had gone wrong in terms of policy prior to the disaster. In this case, whether the airspace above Ukraine should have been cleared, Schoof, in the employ of Rutte, was already involved in handling the disaster and the investigation he had planned was cleverly focused purely on that handling alone (A Schoof unwelcome report appeared, with a tail, but that went too far here. In any case, Rutte was probably very grateful to him for that. The 'reward' came later 😉
Here too, instead of identifying the limited IC capacity in the first phase of corona as a problem and highlighting it as culpable, the focus is once again on the handling (again, remarkably enough, initiated by an NCTV 'er. Coincidence?)
Look forward to the interrogations of Schoof and Rutte, where the previous history and the culpable shortcomings therein will be kept out of the picture at all costs, with the exclusive focus on the settlement. WHILE HERE I STILL M.I. A TEACHING MOMENT LIES.
BUT IT IS PROBABLY: Long live the Rule of Rutte, and: That corona, what a horrible disease it was. We saved what could be saved.
(Armand Gisbers will not be heard, despite Marianne's petition. It's a shame, but even in that case the focus would remain on the handling and not on the cause: the otherwise unjustified panic
(corona was not a horrible disease, we turned it into that with the help of the RIVM). A panic (however unjustified) that is mainly encouraged by the limited capacity, resulting in a lot of unnecessary damage.
I'm considering a Letter to the Editor, focused only on the misclassification.
“Cloneing” as data manufacturing is not useful. It is a reweighted smoothing of the baseline/reference, not of the observed mortality. That is an accepted method, although a stable baseline seems to offer more certainty. However, enormous uncertainty remains, given the small number of members of the group. In short: determining excess mortality in that group remains rather pointless because the uncertainty margins cannot actually be achieved when it comes to safety.
Then: the claim “negative excess mortality is biologically impossible”. Negative excess is normal in relative survival models; the authors themselves refer to it as HVE, so the defense is obvious ('we call that explicitly, right?') and is formally legitimate, if not common. Not something for this specific study, although the phenomenon is only mentioned, not addressed.
Finally, the suggestion that the misclassification unequivocally inflates the VE. This is not necessarily the case: 'the non-consent component works the other way around: healthy vaccinated people "contaminate" the unvaccinated group', will be the defense and then the discussion will turn out that it does not work out that way in this case, etc. etc. Too difficult and that problem ultimately comes down to misclassification. So that will be the subject of the Letter to the Editor: the misclassification. I think I'll give it a try anyway.
Misclassification also seems to me to be the biggest problem within this study of all the problems within the study mentioned above. Moreover, it is a problem that is recognized by the authors (although without consequences).
The failure to attach consequences to distortion (bias, as misclassification) in studies may have to do with the fact that we (scientists) are not taught what the meaning of bias can be. The STROBE guideline (which is used internationally to describe an observational study: see for example here: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0040297), says no more than that you must be clear about the limitations/calculations of your study (including potential misclassification), but does NOT say when the limitations are so serious that publication is better not to be done. This is a loophole of STROBE. A paragraph: how do I deal with fallacies (and misclassification is a form of a fallacy) could help authors realize that science should not be tainted with fallacies or with 'Garbage in-Garbage out'. I think a numerical example that makes clear how serious the fallacy/distortion can be (a total change of result/conclusion) is very useful to show in a note to the editor.
I hope you can use this note when writing the letter to the editor.
If you need a proofreader: you can email me (you know my address). Continued success!
Willem Engel also says in last week's weekly news about a study that "the dead rise in a certain graph" (In my own words). Perhaps also useful, but that is probably what you already mean by the misclassification. My child graduated in 2012 on research commissioned by the Bill Gates foundation. What “should” come out could not possibly be demonstrated and my child was told that he had done excellent scientific work (received a high grade, yes…) but that the same research went to a university in Germany to “investigate” whether anyone there could get the desired result on paper. The positive thing about this story is our critical view of science, which provided many insights at the beginning of 2020.
A fallacy is really different from bias or distortion in a data set. You can build an excellent and conclusive argument on completely rubbish data. The outcome is still nonsense. But it is not fallacy.
With my story I responded to “total bankruptcy of universities and scientists” by Jan and “It is not said that authors are so wrong on purpose” by Willem. Diploma shame has arisen in my family...
At sometime. Bias is worse than fallacy. A fallacy is not true, bias: not even that.
I don't see a hierarchy of seriousness. I think that depends on the impact of the issue at hand. So sometimes bias is worse; sometimes fallacy is worse. But neither has much to do with science.
And both can be the result of a blind spot. But also by design: teleological reasoning and/or data collection.
Science as a method is designed to eliminate both:
Against bias: double-blind, replication, peer review (in theory), declarations of interest
Against fallacies: formal logic, statistical testing, falsification.
Here we know that this did not work out very well in Corona times by the government and "our" "scientific" institutes...
https://x.com/WallStreetApes/status/2066020614117404983