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Sept. 19, 2022 - The Delingpod - James Delingpole
01:16:23
Norman Fenton
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I love Danny Paul, unless another time subscribe with me!
Welcome to The Deling Poz with me, James Deling Poz.
And I know I always say I'm excited about this week's special guest, but I really am.
And I'm really looking forward to talking to Professor Norman Fenton, who is Professor of Risk and Information Management.
Is that right so far?
At Queen Mary University London.
That's correct, yep.
Good.
I've been looking at your Twitter feed of the last 18 months and I've been thinking there is a man who's got to be on the brink of retirement because there's no way that he could be saying some of this brave stuff you've been saying if you were early on in your career, could you?
That's right.
I'm already at retirement age, so retirement is a distinct possibility.
So if it gets to a stage where it's too uncomfortable to carry on the university, then I do have that comfort to fall back on.
It would be career-ending, certainly, to be saying the type of things that I've been saying over the last two years.
from an academic perspective.
Yeah.
We'll come to what you've been saying in a moment, but I have to say it's a horrible indictment of our times that what you're saying is remotely controversial, because all you've been doing is doing your job, which is analysis, isn't it?
You've just been looking at the statistics.
Yeah, we've just been looking at publicly available data, data that comes from Freedom of Information requests, It's mainly government data, but we try and dig below the surface.
You know, we don't, unlike what the government was doing with this data that's available to them, which is just printing out, publishing numbers of COVID cases and seeing these exponentially rise, etc.
We look at reasons why these things might be rising other than The possibility that it's because there's an increase in infection.
Indeed, just as an obvious example, which is one of the things that first concerned me, where I first started to speak out in a way which got me, let's say, into trouble and being censored.
was simply pointing out that in the autumn of 2000 and the late summer, autumn of 2020, when the government was predicting this massive second wave, they were showing day after day, these massively increasing numbers of COVID cases.
A lot of that was down to simply the mass asymptomatic testing that was going on.
They introduced that, that was being introduced at that stage as people were coming out of lockdown and kids were going back to school, etc.
And you have this problem that a lot of those, a lot of people who are asymptomatic and getting PCR tests, those positive tests are not real.
They're not real.
They don't have the virus.
They don't have the virus.
And then they're not.
And many of them never went on to get symptoms.
So they're still classified as covid cases.
And, you know, simply pointing this out.
And saying you need to take account of the amount of testing that's being done.
That was considered, you know, basically dividing the number of cases by the number of people tested, you know, that mathematical division was sort of considered to be an act of treachery, you know, that was sort of... Yeah, exactly.
The sort of thing you shouldn't be doing.
So you've been punished and ostracised for doing your basic job.
But tell me, before we go on, But up until the last couple of years, did you have a kind of... were you just going la-la-la, teaching lessons in maths and statistics and stuff?
Did you have a career devoid of controversy or upset of any kind?
I started to question a lot of things about what was happening in academia, maybe not very publicly, but amongst colleagues.
I was already getting maybe a reputation among some of my colleagues as being something of a maverick for simply questioning things like the woke, you know, the woke ideology and the fact that university, universities and university staff were who are supposed to be sort universities and university staff were who are supposed to be sort of very
And the sort of people who would always oppose censorship and be in support of freedom of speech were actually promoting censorship and against freedom of speech, especially in the, let's say, I'm in electronic engineering computer science department.
And of course, that's, you know, a lot of obviously data science.
That's one of my areas of interest.
And in that area, you cannot believe the extent to which the amount of research going on there is all about doing better censorship of the Internet.
And that shocked me.
And I started to... I did start to question that.
So, yeah, that was... There was also another interesting... There's one other interesting point, I should say, that I... In 2015, I actually co-presented a BBC documentary, it was on BBC Four, on climate change by numbers.
I co-presented it with Professors David Spiegelhower and Hannah Fry.
I don't know if you ever came across this programme.
I probably booed and hissed through some of it because, as you probably know, I've been a big critic of the climate agenda.
Were you the voice of reason in that?
So, I tried to be.
They chose... We were supposedly chosen.
I mean, Hannah Fryer wasn't really known until then.
That was her first big break with the BBC.
Of course, she's gone on to a stellar career since then.
She's their star presenter, but... They chose us ostensibly on the basis... Sorry?
I had my suspicions about that.
You don't just become like that by accident.
But yeah, anyway.
Yeah, so we were chosen because we were mathematicians who hadn't done any work, hadn't done any research in climate science.
We were supposed to be independent.
That was why they chose us.
And we were chosen to kind of present the information about climate change by numbers right so my particular number was 95% which was this supposed 95% confidence that 95% probability that the recent warming was mainly man-made right so that was my 95% now I already had a slight problem with that
Which is a little bit technical and it's not something we need to discuss here.
But that, being on that documentary, was a real eye-opener.
Because I was, I was a little bit, I have to say, I was a bit sceptical.
I don't have any particularly strong feelings.
I was pretty sceptical of the sort of the statistical climate models that, you know, were basically promoting the narrative.
And of course I had, you know, Read relevant works on this and I've been following the sort of the Mark Steen case on this and stuff like that so I was a little bit I was a bit sceptical but this was a real eye-opener because um the bias in the program was was you know almost totally complete and we were everything was scripted this is the thing everything was scripted
And even where I tried to get some of my genuine insights into it, that never made it past the final edit.
So I wasn't particularly happy.
I wasn't happy at all, really, with the things I had to say.
Well, I can't tell you everything because, again, there are still some sort of contractual issues.
I mean, I was playing a pittance, but there is a contract which I think still maybe limits me from what I can really say.
But let's put it like this.
I did voice concerns to the BBC, especially when I found out that some of the information that I'd been scripting was unhappy with.
When I followed that up, the experts who basically scripted it were, let's say, clearly not being totally honest.
And when I raised concerns about this, you could say it didn't go down too well.
And them, BBC, having invested quite a lot, they invested In all of us to have the training, you know, the training to be a BBC presenter, I never heard from them again.
Let's just put it like that.
Whereas, you know, David Spiegelhauser and Hannah Fry have gone on to these stellar careers.
That is interesting.
That will interest a lot of people.
So basically, they wanted your prestige as a professor, a university professor at a big university, but they weren't actually interested in your expertise.
They just wanted the name.
And then you were basically their marionette.
Or they could just put whatever words into your mouth.
And they all are.
It's all scripted.
It is completely scripted.
Yeah.
And as I said, the stuff where I wanted to put some of my own words in, more or less, none of that stuff made it onto the programme.
Yeah, yeah.
Just before we go on, tell me a bit about your... I mean, I'm impressed already that your field of expertise is mathematics and statistics and stuff and probability and things, because these are not part of my skill set.
So, you're obviously quite clever to become a professor of the thing you're a professor of.
Tell me about your career up to this point.
So I was, my PhD was in mathematics.
I eventually went into sort of the computer science department.
I mean, I was first professor at City University.
I left there, I was, I was involved in work Well, it was all mainly about doing risk assessment for what we call critical systems, like things like fly-by-wire aircraft, where there's a limited amount of data that you can use to assess the safety of something like a fly-by-wire aircraft.
It has these very, very high safety requirements.
And for normal safety assessment, They use statistics on failures in tests and stuff like that, and you use statistical models to build a safety case.
But for things like these safety-critical systems, like Airbus, the safety requirements are so high, and the number of failures you might observe in tests is so low that you can't use standard statistical methods for that.
So you have to combine data with knowledge, and that's what I've been doing more or less for the last 30 to 40 years.
We use a sort of Bayesian statistical approach, Bayesian probability, which enables you to combine Expert judgment, knowledge, as well as data, even when the data is very limited.
So you can actually come up with kind of like auditable and justifiable, as in that case, safety arguments or arguments about risk.
You can quantify risk.
And the difference between what we do, actually, and what a lot of the number crunching statistical analysts, sort of government analysts do, is that we don't just do number crunching.
You know, we do then, it requires going into the data, seeing what data is missing, understanding more about why the data you're seeing is what it is.
Is it due to missing data, confounders, biases?
We're able to incorporate all of that stuff into our models.
We end up, of course, when we make predictions, which we do, we do provide quantified predictions of risk and safety and stuff like that, what you'll find is that we express full uncertainty, unlike, you know, the sort of, oh, there's going to be, you know, 500,000 deaths in the next two months.
We would have, we knew straight away, I mean we knew, look at, we knew how they do their models, that they hadn't captured all the uncertainty about the key parameters.
Right.
So we knew this could be, you know, you know, it's going to be, it was likely much lower and of course a much greater bound of uncertainty.
They were never clear about these bounds of uncertainty, whereas we, we make that, you know, we make that very clear in all of that.
That's what our work does, it expresses the full uncertainty of the knowledge that we do have and the predictions that we can comfortably make right that's what i've been yeah that's that's what i've been doing really for the last well probably at least 30 years working in those areas because it it turns out that there's a lot of although we started in the safety you know so a lot of it was originally in transport safety the These issues are everywhere.
They're in all kind of like government decision making, medical, done a lot of work.
The reason why I got involved in the COVID work at the beginning was I was already principal investigator of a big collaborative project, which was looking at improved decision making, prognosis and diagnosis of chronic health conditions.
We work with clinician, you know, expert clinicians in these different areas.
So, And again, all of that is about making better use of the limited data we've got by ensuring that you've got appropriate expert knowledge, in particular, causal knowledge.
And so it's kind of inevitable that I get involved in the COVID data analytics.
I wasn't going to ask you about the Bayesian element and what it all meant, but actually you've kind of explained it, and also you've helped me understand why you are such a thorn in the side of the government.
Because would I be right in thinking that The governments don't really like people like you who don't just crunch the numbers but look at the whole issue in the round from different angles.
They'd much rather have somebody like Neil Ferguson who basically crunches the numbers to give them what they already wanted to know.
So, what's it called?
Policy-driven evidence-making.
That's what they want.
Yeah, you're right, but there's a difference between the stuff that Neil Ferguson does, and let's say the stuff that the... I wouldn't call him a number cruncher as such.
The people at the Office of National Statistics, and incidentally, I've got a bit of a bone with them.
I think that they've played a that they used to be quite objective and neutral.
They've become a problem during this.
They are real number crunchers.
They simply they're the ones who really don't take account of these.
Look at these sort of hidden what's missing or the biases.
They really don't.
They are just straight number crunchers.
That's all they do.
The thing about Neil Ferguson, he they do some more.
They do.
They do more modeling, of course.
So they're not doing straight number crunching.
But the problem is and it gives us a bad name because what we do is it's also modeling the prediction.
But the difference with them is that they have these they're like these massive sort of simulation models.
It's a bit like, I don't know if you knew that, computer games like SimCity.
It's a bit like SimCity on steroids where you're trying to model You know, tiny, minutiae in the population as a whole, but it was critically, and so it's quite fancy modelling, but it's all dependent.
Everything was critically dependent on a few key parameters, which they completely got wrong, you know, about, you know, they didn't allow, for example, in any way for the correct probability that the people who, you know, got the virus wouldn't become ill, you know, that these people, the number of people who wouldn't You know, actually pass it on to others, even if they were infected.
So they missed out, they completely underestimated all these key parameters, which led to these ridiculous predictions, OK?
So... Yes, but... And that's the problem.
Do you not suspect that actually Ferguson was doing what he was required to do?
You know, they needed the fig leaf of the science to justify it anyway.
Yes, yes, they did.
And remember that if you look at the makeup of the SAGE academics, right, the modelers and the cognitive, you know, behavioural scientists, they also had a particular, they had almost universally, a particular worldview.
I won't go into the details of that because I think you know what I'm on about here, You know, actually naming names.
And I think that their worldview, they're kind of interested in, you know, more government control and that type of thing.
So they already fitted in, you know, this was an opportunity.
I think that a lot of those people, it was a mutual kind of like benefit.
You know, those people saw this as an opportunity, not just to Promote their own work, but also to promote their own kind of narrative on the world.
I mean, I was shocked.
One of the things that really concerned me fairly early on in this, before I was a sort of cast out and persona non grata, I was actually invited onto quite a few panels and stuff like that.
And I was on a panel with another set of academics.
And that was the first time I really heard about the Great Reset.
Right.
And all these other academics on the panel there, they were really, they thought it was fantastic.
This is when we were in hard lockdown in sort of the, you know, late spring, early summer of 2000.
20.
And they were saying, they all were saying, effectively what Klaus Schwab was saying, that this is an opportunity for Great Reset.
They were using the words Great Reset, all the other academics on this panel.
And they were saying things like, it's wonderful that, you know, since the lockdown, we've had these, you know, the air's cleaner, we've got these decrease in carbon emissions, and this is something that we need to keep up.
You know, so they were, they were very excited by this.
I mean, academics A lot of, it wasn't just those academics, a lot of academics who were promoting and very strongly in favour of the sort of hard lockdowns anyway were the people who were least affected by the negative aspects of the lockdowns.
You know, generally sitting there comfortable in their, you know, their big homes with gardens, you know, their laptops they can carry on, you know, didn't have to travel into work.
It was very convenient for these people.
Yes.
What was the committee that you were on?
No, this was like... I'm just talking about panels that I was on.
Oh, panels.
Sorry.
Right.
OK.
Yes.
Well, I suppose... Actually, I can give you the link.
The trouble is I'll then reveal the name, but there is a video of a panel that I was on with three other academics, which is actually... I think the video is still available, so you can see the kind of narrative that the others were building around the Great Reset.
I mean, do you think that these advisory panels that they put together, you know, like SAGE and stuff, do you think they would automatically have screened out people like you because they would have suspected that you're a politician?
Oh yes, absolutely.
Absolutely.
Yeah.
I mean, even once Because as I say, at the beginning, we, we, incidentally, we were the first.
I think we were, me and my colleague, people like Professor Martin Neal, Mag Dalsman, Scott McLaughlin, so these are people I know who I've worked with over this.
We We were the first, I believe, to have a peer-reviewed article in a journal which actually said that the Covid infection fatality rate, how deadly Covid was if you called it, was much lower than was being advertised and widely said at the time, while the infection rate was much higher.
So COVID was more widespread, but far less deadly than was widely being claimed.
So we had that paper published, I think it was in Journal of Safety Research or something like that, fairly early on.
And that wasn't considered that outrageous at the time.
We got it published, that was no problem.
But it was only later Right, later that summer when I started to raise the questions about the actual, you know, the problems with the PCR test inflating COVID cases, and therefore inflating COVID hospitalizations, inflating COVID deaths, that was when it started to turn.
And so I was getting Um, you know, I had been regularly invited into things like, you know, to talk about this at Queen Mary, our medical school and other places as well, but at that point it was interesting because I'd actually been invited to give a talk to, um, the Wolfson Institute, our medical school, and suddenly for the first time in my life I was, it was cancer, I was cancer that was decided
There was sufficient opposition, I think it was less than 24 hours before the talk was due to take place, that they cancelled me.
And that was quite a shock.
You would have thought, because this puzzled me as well, I mean...
You would have thought that somebody who had produced a peer-reviewed paper early on saying that the IFR was not nearly as high as the doomsday scenario said it was, you'd have thought that would be seized on by governments etc as a way of putting a bit of perspective on the whole.
Because clearly if Covid wasn't a kind of Horrifically different from any other viral respiratory infection.
Then there was no need to panic, was there?
So what happened to your paper?
Did you just get buried or what?
I mean, it's still there.
It hasn't been withdrawn.
It hasn't been withdrawn.
I've got colleagues, close colleagues, who've had really, really good papers about either issues with the vaccine or papers on alternative medical treatments, alternative, you know, treatments.
Which had gone past peer review and even had been published online and then were actually removed, were taken off without explanation.
So, I mean, at least one of those are subjects of legal case.
I can't go into the details about those, but there are several examples like this of papers from people I know well, colleagues I know well.
My work, After that September, after about September 2020, when we were, we submitted, we've written a lot of papers since then.
Loads of them.
Loads of different analyses, right?
It no longer, we sent them to all of the top journals and as well as not so top journals.
They get rejected without review.
As soon as they see my name on it, that's it.
They'll come out, so the Lancet or British Medical Journal, Well, simply, they'll say things like, it's not of sufficient interest or it's out of scope.
Right.
You think that during that COVID crisis, if you're raising serious statistical issues about the way the COVID data is presented, you wouldn't think it would be just ruled out as being of insufficient interest or out of scope, but it was.
And it's even worse.
You know, there are these preprint servers, like Archive and MedArchive, which basically, anybody Anybody can put their stuff on there, because it doesn't get reviewed.
That's the whole point.
They do have a screening process to check that there's not plagiarism, to check that it really isn't outside the scope.
But that's about it.
Otherwise, it's automatic.
Those were also automatically rejecting papers with my name on them.
So the only place I actually stopped even bothering I mean, I have had some other, like, there have been some other stuff published, but in general, the only way I get my stuff out there is I put it on ResearchGate, which is a pre-print server where there's no screening process.
It goes up immediately.
Nobody turns, you know, you can put anything up there.
But the good thing about it is I've actually had almost a million reads of those papers since I put it up, which is far more than I'd get From a journal, you know, a peer-reviewed journal article, so it's not all bad.
That's... no, because you seem quite cheerful about it, because I mean, I think a lot of... But again, ah, but it's... but it comes back to it's because I'm near retirement age.
I accept that I'm not going to get my papers published in top journals anymore.
That would be a massive problem if I wasn't close to retirement age.
Yeah, yeah.
I can see that.
So, tell me... That's why it doesn't bother me.
I've got over 350 peer-reviewed papers.
I've done seven books.
So, you know, if they reject my stuff now, so what?
The fact that I can still get it out in some form at the moment, that's great.
So, tell me about some of your findings.
I mean, I know that you were... Well, for a start, you're sceptical about the figures on the percentage of the population which has been vaccinated, aren't you?
Oh my God, this is completely bizarre.
We've been on to this well before the BBC2 documentary called Unvaccinated, which has reignited this issue.
We were on to this because a lot of what we've done in the last year, since the vaccine was launched, has been looking at evidence of efficacy and safety of the vaccine.
And to do that properly, You need to have comparisons of number of cases, number of deaths between the vaccinated and the unvaccinated.
You've got to have that division.
So in all the efficacy tests, you're looking at the number of people who get infected, who get COVID after being vaccinated.
Sorry, the number of people who get infected With COVID, and you're looking at the numbers in the unvaccinated and comparing it with the vaccinated.
Yes.
Right, so in the Pfizer trial, when you've got a randomised controlled trial like Pfizer did, you're supposed to have the same number getting the treatment, the same number getting a placebo, so they don't know what they're getting, right?
But of course the people in charge of the trial can then compare the number infected who are in the treatment arm with the number infected in the placebo arm, right?
Now in that case, because you know the numbers are equal, Then, in each arm, if you see more in the placebo arm than the treatment arm, then you can say that the vaccine is effective.
And the extent to which it's effective, of course, is dependent on the numbers.
Now, setting aside the fact that the Pfizer trial and those numbers is another thing, how they got to their so-called efficacy with those numbers, that's a different thing altogether.
But critically, since the rollout of the vaccine, we haven't got a randomised control trial.
So what we have to do Simply look at the total number who are vaccinated who get infected and those who are unvaccinated and get infected.
And in the simplest terms possible, You're dividing the number of vaccinated infections by the number of people vaccinated, and you're dividing the number of unvaccinated infections by the number of people unvaccinated.
That's one way of doing it.
It's a simple division.
So you have to know how many there are who are vaccinated and unvaccinated.
Now, the problem is, you'd be amazed how poor the information is about that for the UK adult population, right?
Because to show how inconsistent it is, the Office for National Statistics claims that it's 8% of the adult population, which is 4 million.
But if you look at a different UK data agency, the UK Health Security Agency, on their data says about 20% of UK adults are unvaccinated.
And people, I won't say who, but people say, oh well the ONS, they're more reliable.
Why?
There's no evidence that the ONS is more reliable.
In fact, we know because of our analysis all up to this about, in particular with vaccine safety, we know, we absolutely know that the ONS is underestimating the number of unvaccinated.
We know, we always knew it was a higher figure.
We knew it was a higher figure than 8%.
We knew that.
So if you look, for example, well, we know for two reasons.
One, when someone is, if somebody dies shortly after vaccination, Although they say that they're classified as a vaccinated death, they're not.
They don't get recorded.
So they get put into the... In that case, they get put into the unvaccinated.
So there's things going on there, right?
But the key thing is, if you look at their own estimates of the compared mortality rate between the vaccinated and the unvaccinated for non-COVID deaths, in their most recent report, the non-COVID mortality rate for the unvaccinated is about 60% higher than for the vaccinated.
And when you think about it, this cannot make sense.
It can't make sense because this is non-COVID deaths.
So they're saying that either there's some massive bias here, and bias is of course in their estimate of the number of unvaccinated, or COVID is this miracle cure which is stopping all these other deaths which have got nothing to do with COVID.
But we know it's not that because the vaccinated mortality rate is well below the historical mortality rates for the given period.
So we know that that's wrong.
We know that that's being underestimated.
And the unvaccinated mortality rate is way higher.
It's almost twice as high as the Historical mortality rates.
And the explanation for this is they're massively underestimating the proportion of unvaccinated against the vaccinated.
Because they're simply taking the wrong denominator.
The denominators are wrong.
But here's the crux.
Here's what's great.
In the BBC2 documentary, they were promoting it as this For, you know, 8% of the population.
And they were promoting it, actually, that it was still 4 million adults, right?
So it's a small percentage, but it's a large number.
But what they're trying to do is create a narrative that it's only 8%, right?
So although it's 4 million people, it's a tiny fringe minority, sort of, basically, they're crazies.
Yeah.
It's a tiny fringe minority of crazies.
So that's a narrative.
But here's the thing.
For the programme, They commissioned an ICM survey, a very large survey, in fact the largest survey of its type.
There's actually over 2,600 people, which is big for a survey of this type.
A very, very representative survey.
We've gone through the details of it because it's all available online.
We can look at the raw data.
And what do we find in that data?
26% of adults, UK adults, were unvaccinated.
Now, whether you do classical statistics number crunching or Bayesian statistics, whichever way you do it, there is no way that if in a survey that big, and it genuinely was a proper representative sample, it was representative of all age groups correctly, even against the ONS population age groups, all the social classes, all the different areas of the UK, it was almost a perfect match all throughout.
Right?
You can't, there is no way you're going to get 26% unvaccinated and yet the true population percentage somehow is 8%.
It just can't happen.
In fact it's almost impossible for it to be less than 20% statistically given a sample of that size.
And yet they're still saying, ah, well that's somehow, somehow, yeah, they accept it's representative, but the thing it wasn't representative of was the number of unvaccinated.
So they, they put in, they actually changed the weighting for the unvaccinated back to 8% because that was the ONS estimate.
So what do you think, because, I mean, the Office of National Statistics used to have a reputation as being pretty kosher, didn't it?
Pretty, pretty reliable.
What do you think happened?
I don't know whether there's a narrative there.
I mean, we have spoken to the people who produce these vaccine surveillance reports.
We've been speaking to them.
We spoke to them first over a year ago because we were concerned there were issues coming out which we were already identifying were problems with their data.
OK, and at first they were very helpful because at first there wasn't any raw data according to different age groups because you can't basically do this assessment of safety and efficacy By looking at the total numbers, you've got to do it by age group, right?
Because obviously, otherwise it's actually unfair against the, it can bias against the vaccine because early on, most of the people, you know, it was mainly older people who were getting vaccinated initially.
So you're going to expect higher mortality rates in the vaccinated compared to the unvaccinated, right?
So we're aware of all that.
And at that time, people were wrong.
People, you know, who were the sceptics, there were sceptics who were saying, look at this, you've already got this massive evidence that the vaccine's killing people.
No, that was wrong, because they were making the mistake of not taking effect of this age confounding, right?
So we were, you know, we weren't making any claims like that at the time, even though we were accused of making claims like that, which is another thing, but we never did, right?
Well, I certainly didn't, not my group.
So we were engaging with them and we were saying, you know, can you give us more detailed data, like age classified data?
So they did.
They started to release some slightly more sophisticated and age categorized data and then we found some other problems.
And then, bit by bit, they were releasing more, but then it came to this thing where it was clear, because of this problem, we found this problem whereby the vaccine was apparently having this, when we saw it on their data, at points where the vaccine was being rolled out in each age group, right, at its peak, so as, for example, if you take, for example, any age group, take for example, in their data, if you took, let's say, the 60 to 65 year olds,
What you were seeing is, as the vaccine rollout peaked for that age group, there was a peak, at the same time, in non-COVID deaths of the unvaccinated for that age group.
Therefore, we knew this was a... That particular problem comes about because they're misclassifying people who die shortly after vaccination as unvaccinated.
So we knew that, as well as these other... Yeah, within 28 days.
Is that right?
So, there's two things.
They're two separate things.
With the mortality, they are, they claim...
The ONS claim, no, that if they die within, even a day after they've been vaccinated, for those mortality data, the all-cause mortality data, they are supposed to go in as classified as vaccinated.
But they're not.
They're not in most cases.
They don't.
So people who die shortly after vaccination don't get classified.
They get classified as an unvaccinated death, rather than a vaccinated death.
What you're maybe confusing it is, if they die within 28 days of a PCR test, irrespective of what they died because of, irrespective of the cause of death, they are classified as a COVID death.
But that's different.
I'm talking about simply the surveillance report where we want to determine the safety of the vaccine by comparing the vaccinated and the unvaccinated.
So it was clear there was a problem.
We knew there was a problem with their data.
They knew there was a problem with their data.
Do you know what they came back with?
They said they came up with this theory.
Yeah.
The theory was they called it the healthy vaccinee effect.
The reason that the unvaccinated were dying of non-COVID causes at the time when everybody else was getting vaccinated was because they were too unhealthy to get the vaccine.
Right, they were kind of like two moribund.
And we know that, we know both empirically, we know both theoretically that doesn't work out because we tried running models which took account of that.
You can't, you don't, you can't get the data that's observed with that.
But we know empirically it's not the case because We know that actually people who were fairly close to death, the really seriously ill people in care homes, for example, and others with major comorbidities, they were prioritising the vaccine.
They actually got it first.
So the idea that those people were denied the vaccine because they were close to death is a complete fabrication.
If anything, it's the other way round.
It's the other way round.
I'm dying to hear what you've found about vaccine efficacy and mortality and side effects and things.
Do your statistics that you've seen show that the vaccines are safe and effective?
No.
Now, I don't go so far as to say that I think the vaccines are causing a massive number of deaths or anything like that.
What I can say now with reasonable certainty, but I can say that there is no evidence, there is no evidence that the vaccines reduce all-cause mortality.
And if anything, it might be slightly increasing all-cause mortality.
Now the reason why, that might not sound like a lot, that might not sound like an important or significant statement.
I'll tell you why it is.
If COVID was as deadly as was always claimed, And if the vaccine was as safe and effective as is claimed, then what you should be seeing is a large reduction in COVID deaths for those who are vaccinated compared to the unvaccinated.
So you expect to see a significant drop, right?
And on the other hand, if they're as safe as claimed, We should only be seeing a very small, if negligible, increase in non-COVID deaths in the vaccinated.
So when you balance these out, what you should be seeing overall is the COVID vaccines, whereas if COVID is as deadly as it's supposed to be, as the vaccines are safe and effective as they should be, you should be seeing a higher all-cause mortality rate in the unvaccinated.
But we're not.
We are clearly not seeing that.
And if anything, it's slightly the other way around.
And therefore, there is no evidence that the vaccines are... There's no evidence that the vaccines... If there's no evidence the vaccines are reducing all-cause mortality, which there isn't, Then why the hell, what the hell are we doing?
What are they for?
We know, we know they're no longer, we know they're not effective at stopping infection transmission.
We know now that the more boosted, the more vaccinated you are, the more likely you are to be, you know, to get COVID and to transmit it as well.
Yeah.
We know, look, you don't even, do we really need data for any of this?
Just look around.
You know, I'm sure you know lots of unvaccinated people and you know lots of vaccinated, as I do.
All of my vaccinated, heavily vaccinated friends, right, are getting COVID over and over again, repeatedly.
Yeah.
I don't know of a single unvaccinated person who had COVID naturally, which I mean I did for example, who've actually got it again.
I don't know any.
So what more, people can see the evidence with their own eyes that these things are not effective.
Yes, yes.
That extraordinary thing... So what is the point?
I mean... Sorry, go on.
No, no, no.
OK, so that extraordinary thing you described, where you get this peak of vaccines coinciding with a sort of peak in deaths.
You said deaths, didn't you?
Yeah, of people who don't take the vaccine.
Yeah, OK.
Now, even I, as a non-statistician, could see that that is Obviously, obviously wrong.
Now, you can't be the only professor specialising in that area who must have looked at this stuff.
So why aren't they all crying wolf?
It is incredible.
Look, there are a few, in fact a couple of really senior and important statistics professors who have contacted me privately to say they support what I'm doing but they can't go public because it would damage their careers, they know that.
It's the prominent ones who actually have been promoting a lot of the junk data.
This is what gets me.
People, I'm not going to name names here, but people are following this.
It should be fairly obvious.
But, you know, the senior, you know, senior people in sort of statistics and risk could easily should be seeing this.
But they're just they're just completely not only ignoring it, but they're putting out the kind of like the alternative that the alternative safe and effective narrative.
I mean, I said, after this latest ONS report, where I just put, I said, all you need to see about this to know that this is, to prove all the stuff we've said about this, why this is junk, just look at their non-COVID, these non-COVID mortality rates they're saying, whereby suddenly Now, the unvaccinated have got this life-enhancing, non-COVID, where they're benefiting massively.
They suddenly can't die.
They're not dying of non-COVID things, whereas the people who didn't get the vaccine are suddenly dying at a much higher rate of non-COVID.
I mean, that tells you straight away.
You don't need to do anything else.
Just look at those data.
You know, whatever it was.
I put it on my website.
You just compare it.
It's just two numbers.
It's the rate for the unvaccinated dying of non-COVID mortality and the vaccinated dying of non-COVID mortality.
They should be the same or very slightly higher for the vaccinated.
They've got to be.
There's no reason for them not to be more or less the same, and yet they're completely different to the historical mortality rates for non-COVID deaths.
So there's nothing more you need to look at to know that all of the data on which all of the claims of safety and efficacy are based are based on that flawed data.
Which is getting the numbers, which is basically classifying, misclassifying people who are unvaccinated, than who are vaccinated.
Misclassification problem.
And they don't speak out about it, and I'm ashamed of the profession, I'm ashamed of these people, because in years to come I hope that it will become even more obvious than it is now, and that these people never said anything about it.
Well, you've...
I couldn't believe this.
You've described how you've had students on your course refuse to be taught by you on a compulsory module because you're a vaccine denier or something.
I was called an anti-vaxxer.
Tell me what happened.
I was called an anti-vaxxer.
Again, this is- I can't go into the full details of this, but... Okay, I was actually, um...
I was actually having some emergency heart rate, I had to have a couple of stents fitted and I was sort of in the recovery in the hospital and I got an email from the head of schools saying that a delegation of students was extremely unhappy with me and wanted to deregister from my module and I had to do something about this.
And I won't go into details, but let's just say I didn't get, you know, the students were the ones who would have believed it.
I hadn't said absolutely nothing.
I mean, I do a module on risk and decision making.
It's a master's module to over 250 students.
It's a big module.
It's actually the fact that 10 or a dozen or whatever Decided even before I actually taught them anything, even before I'd said anything.
And I didn't say anything controversial either about this.
In fact, the only lecture I'd had then...
Well, I'd only had one lecture, and the only thing I said about the vaccine was, funnily enough, I used that example about the confounding factor on age, where I was saying that the anti-vaxxers had wrongly said that this was evidence that the vaccines were unsafe.
That was actually the classic statistical example paradox called Simpson's Paradox, which is always, I always give an example like that in my first So, I didn't say, you know, I wasn't even, it was unbelievable because I'd already, let's say, got the reputation, even before they'd been on the module, being, let's say, somebody from the alternative narrative to the mainstream narrative.
The students decided that, you know, as a spreader of misinformation, anti-vaxxer, and a danger in the sense that this is the thing, students and other academic staff as well, they are afraid of having an alternative narrative presented to them.
It's like, it's a fear, it's a weird thing.
It's like, why, what are you afraid of?
I mean, and yeah, just to say I wasn't, you know, I won't go into details, but there wasn't, let's say, a lot of support for... What, you didn't get a lot of support from your, from your university?
Yes, let's just put it like that.
That's really bad.
Because, look, I mean, when I was at university, many years ago, An incident like that would have been treated as, well, these are scrotchy students, they're 19-year-olds, 20-year-olds, who cares what they think?
You know, grow up and if you don't like it, just don't turn up or you'll get sent down.
No, I was told that I had to change my style, that it was me who was, you know, that these are students who have to be listened to and therefore, you know, I have to Be the one to sort of take the, you know, take the hit.
I can tell, just some of the things you've said, talking to me, I would very happily recruit you as my personal maths tutor and stuff, because you're good at making complicated things that my brain doesn't compute very easily seem very simple.
So I can see that you're very good, you know, it'd be a privilege to be taught by you.
But What you've described seems to me to align with, I think, people in different departments have experienced across academe, which is this sort of the replacement of academic rigour with just kind of woke politicking, which seems to have just overwhelmed everything else.
It is.
I mean, I was actually on Brett Weinstein's Dark Horse podcast a couple of weeks ago, and he actually gave a very good analogy here, talking about whether, because we were talking about academia and these kinds of problems, and whether it could be reformed.
And he said, no, academia now, you have to treat it like a rabid dog.
There is no way.
Shoot it.
It's got so far.
Yeah, basically, that's basically what he said.
Those are his words, of course, not mine.
But yeah, but I think, look, what else do you do with a rabid dog?
You do shoot it.
I think that's absolutely right.
Tell me, is the standard of undergraduates that come up now as clever or well-educated or motivated as previous generations, would you say?
Or is it getting steadily worse?
And I think it's, there's a decrease in the level of creative thought amongst the students.
And I only teach now at master's students and a lot of those, you know, quite a few of those are overseas students as well.
And we get some, I mean, I've had some fantastic, I've got some fantastic students on my module.
I mean, I've just done my, fortunately, I'd say that out of the 200, over 250, a lot of the students really did appreciate my module.
I get very good feedback.
In fact, you can see this by modal distribution.
You've got this small number who are rating it like the worst possible rating, who we know who they were.
But actually, the vast majority are giving it like top marks, OK?
And I've got some really good, let's say, some fantastic project students.
So there are still very, very, very, very sharp students.
But generally, generally, I think the level of sort of inquisition That you get overall, I think, and their ability to think outside the box, to think sort of in abstract terms.
You know, they don't a lot.
The difference is they don't.
They want to be generally told very, very precisely.
and methodically what they have to do step by step and they don't like it if they're kind of like left if you if you give them set them saying sort of slightly more challenging whereas in maybe a previous you know in years gone by you you tended to get a lot more who were kind of like welcomed the idea of being challenged let's say more deeply yeah so yeah that sounds pretty pretty bleak and the
All the fellow academics, the people at your level, surely it's not beyond... I mean, they're close to retirement too.
They've published all the papers they need to publish, they don't need to...
Surely, look, let's get back to basics, because what you're describing here is something that people like me in every trade and profession have felt.
I, as a journalist, feel that none of this bad stuff would have happened if journalists had simply done their job, the reason they became a journalist, to tell the truth.
You're a statistician.
Your line would be, well, if the statisticians had done their job, we wouldn't have these junk statistics.
And you can say the same about doctors.
You can say the same about vicars.
They should have kept the churches open.
And yet, so many people haven't done it.
How can you be somebody who spent their whole life in statistics?
You go into academia because you're interested in that kind of stuff.
How can you then betray everything you've studied all this year for by just publishing bollocks, is my question.
Because the people who just, you know on Twitter, the people who call themselves epidemiologists, the statisticians who are promoting the fear about the COVID data and they're doing it and they get very, very, and they've got, you know, because of the whole way Twitter censors and boosts, censors people who are critical of the narrative and boosts people who are Uh, who are supportive of the official narrative.
These people are the ones who get, these are the ones, they're the people who get seen on Twitter, they're people who get invited onto the BBC.
I mean, just, you know, they're, and they are talking, they are talking the data.
They're claiming, look, the data's telling us, oh, it's going to be, we're going to have to lock down again.
You know, you've actually got these people still saying things like that, and we're going to get a new wave, and this kind of thing.
And the vaccines are really, really Absolutely safe and we've got to inject babies and stuff like this.
There are many people who are statisticians and data analysts, academics, who are pushing this.
My view is there is a distortion here.
The distortion is that those people have been able to be seen to be Part of the, let's say, the consensus.
Because they have been able to, they've been promoted, because they are sending, they are what they are, because they are pushing the message that, let's say, the Great Reset people, and everybody else in that, want to hear.
That is the message, that is the narrative that they need.
It's all part of... And that's why you're seeing that.
I think that there probably are far more people than we think who are sceptical Right?
That even at retirement age or whatever are simply afraid to speak out.
But what you were describing then sounds to me like something I've... I've been watching this going on for at least the last decade because before this I was onto the climate change scam.
And what you're describing to me sounds very much like the phenomenon of noble cause corruption.
In that these people have decided what the right way forward is, which is the Great Reset, and so it doesn't matter that they're lying because it's for a good...
Right, I'll now tell you something.
I'm not going to tell you what it was in relation to, and I'm not going to reveal a name, but a very, let's say, a very senior statistician, when discussing some of these issues, I'm going to say what the issue was, actually said the word, when I challenged this person why they said or did what they said, his words were, we have to lie for the greater good.
And I was shocked by this, because that's the first time I'd heard it.
And I told another couple of colleagues, who I thought might be also shocked.
And do you know what their words were?
He's right.
So, I mean, this is it.
It's terrible.
That is where we are at.
That is gold, what you've just said, then, because it just says it all, really, doesn't it?
And also, it actually fits in perfectly with what we said about the way that academe, across the board, it's not just statistics, has moved from rigour and focus on the actual alleged subject to promoting a kind of an agenda.
It's pushing a narrative.
It's pushing a particular narrative.
It's pushing a particular worldview.
It dominates.
As I say, I mentioned in the field of data science, I'm horrified at the amount of research funding that is being spent on, effectively, intelligent censorship.
It's incredible.
Oh yes, explain that a bit, we didn't explore that one.
Okay, I was a fellow of the Turing Institute, because when Queen Mary joins, the Turing Institute has got a lot of funding to make the UK the leaders in data science, right?
I was very, very disappointed to find that, you know, that a lot of the research funding there was being spent on this finding misinformation online, so the misinformation thing, automatic Um, discovery of misinformation online, who is promoting misinformation, and how to stop misinformation.
Well, of course, that's all well and good, but who decides what the misinformation is?
And what I saw, talk after talk after talk, you can imagine, I don't need to tell you what their examples of what misinformation was.
It was basically Trump, Trump, Trump, Trump, Trump, and this was before COVID even.
And then after COVID, it was anti-vax, anti-vax.
These are all of their examples, the only examples they have of misinformation.
And you've got this massive investment of effort into basically finding everything that might be misinformation according to those classifications, and the people spreading misinformation according to those classifications, and stopping it by all intelligent means.
This is the amount of money, I mean, the Online Harms Bill and the AI that's going to go into that.
It's all... It's a massive amount.
You cannot believe how much research in AI is invested in looking for examples of so-called inequality and racism.
Really?
How does that work?
Sorry?
Well, how... Again, this is...
Data science.
So you're looking at data, whether it be crime statistics or sentencing statistics and all of that sort of stuff.
you know, all different types of home office data, for example, and trying to find examples, you know, literally desperately trying to find examples of where what you're observing is due, the data you're observing is due to systematic racism.
Right.
So you mine data to look at looking for examples of offence.
You're looking for offence.
Yeah.
And I'll give you another shocking example.
Attending a, you know, watching one of these lectures at Turing Institute, the guy who's an expert in these predictive algorithms, right?
Right, so you're trying to predict, you know, let's say a controversial example would be predicting whether a criminal should be released on probation or not, let's say, for example.
Okay, in terms of whether they're likely to re-offend.
Okay, so it's a quite common one.
There was quite a lot of controversy over this issue.
So you can come up with algorithms which are very accurate in their prediction based on the personality traits.
Not completely non-racial, just other Because you never have things like race or even, you know, sex in there, but you can come up with things which are quite accurate predictions, right?
And this guy was saying, yeah, it's too accurate.
His model was too accurate because it meant it was biased.
It was more, in his example, blacks were more likely to be predicted to re-offend or something like that than whites.
And so what was his method then was all about Making the algorithm less accurate to de-bias it.
I kid you not, this is what the research was doing.
It's not a surprise that after two years, because we have to reapply to be Turing Fellows, At Queen Mary University, suffice to say I'm no longer a fellow of the Turing Institute.
You're a bad, bad man.
What's left of your career, has it been destroyed by your unhelpful truth-telling, or what?
Well, I certainly wouldn't be able to.
I was quite successful in getting grants before, and there's no way that's – because there are people who've worked on grants who refuse to be associated with me at all.
They won't have their name on a paper that I'm co-author of, let alone be on a collaboration on a grant.
So that's out of the window, especially the type of work we do.
You need – it's interdisciplinary.
You need – especially a lot of our work was in the medical applications.
There's no way I can ever get grants again, even if I wanted to.
Papers as well.
So that's the problem.
But again, it comes back to seeing, am I really bothered about it?
No, because I've got the option of retiring.
Yeah.
But also, you said that your papers, ironically, are more read now than ever they were when they were published in the regular.
That's one good thing about it, yeah.
That is interesting, because we tap into It's tapped in, you find that you've got a different audience, an audience who are interested in finding out these kind of differences of opinion from the narrative that's being pumped out by the government.
Yeah, but where does your research get, I mean, in the media?
I imagine you get ignored by the mainstream media, don't you?
Yeah, I mean, Well, it depends what you mean by ignored.
I mean, there was this other funny thing that's happened recently.
It's not that funny.
I don't know if you've heard about this Wikipedia incident.
Yeah, tell me.
So basically, I was...
Due to give evidence in a, in court in Jakarta and Indonesia, it was all done by Zoom, obviously couldn't go there, on quite an important case, but it was one of, I think, that the plaintiffs told me that this is the first case brought against an actual government, against a government which actually got to open court in front of judges, they don't have jurists in front of judges, regarding their vaccine mandates.
And I was called as an expert witness for the plaintiffs to provide evidence, you know, this kind of evidence about the efficacy and safety of the vaccines, right?
So that was due to take place, that was whenever it was in July, but about a few days before I was due to give the evidence, the plaintiff's lawyers pointed out that my Wikipedia page had a major entry called COVID-19.
It was never there before.
I mean, I don't really look at... I haven't really looked at my Wikipedia page for ages, but apparently, shortly before that, sometime in the spring, suddenly there was this big COVID-19 entry added to my Wikipedia page, saying, COVID-19 misinformation.
And it said, in March 2021, Fenton was one of several academics who put their name to a document seeking to persuade the British government not to pass COVID-19 legislation, suggesting a large increase in deaths being caused by the COVID vaccination programme.
And then it quoted someone saying that we're ridiculous, I'm a ridiculous person, whatever, what's that effect?
Now, it turns out, it turns out that that claim The only reference they gave to that claim was an article in the Times, which had been published in March 2021.
And the article in the Times, which was different.
The online version of the article changed a few times, actually.
This is another weird thing about it.
But it was an article by the Times science editor called Tom Whipple.
Have you ever come across this guy?
Yeah, terrible.
Absolutely.
Yeah, exactly.
One of them.
And he had completely... It was a complete... What he said there was a complete misrepresentation.
I mean, I've never... It was actually a document that was produced by the Hart Group.
Which has, on the inside cover, a list of all the Hart members.
There's lots of them.
There was at least, you know, 40 whatever.
And there was my name.
It was basically a compendium of papers.
One of which was a paper by a guy called Joel Smalley, where he had been one of the first to actually point out, all he did was point out the early data on vaccines in January, February of 2021, where he was pointing out the possibility that there might be a coincidental relationship in spikes of deaths after vaccination.
He didn't draw any causal conclusions.
He didn't say that vaccines are causing massive deaths or anything like that.
But because there was that one article, Right?
My name wasn't on any article.
I didn't even know this document was out.
Yeah.
Right?
Because it doesn't work like that.
We're just a group of, you know, we're not all one-minded people or anything.
It's not a committee that inspects everything that goes out or anything like that.
You know, I just attended the meetings online and stuff like that and I met with some of the people.
Now whether or not I agreed with that article, my name wasn't on it anyway, and it wasn't saying what he said.
And he put in the Times, because I was one of the more prominent professors of the Hart Group, I think he named me and a couple of others, I was then, in his article, was one of the people cited as making this statement, making this claim about putting my name to this statement, which of course was a complete fabrication, a complete misrepresentation.
And yet, this was the basis for this.
To cut a long story short, there was then a Wikipedia war went on, which I wasn't even aware of, where anybody who tried to change it There's a guy, there's a kind of like a set of gatekeepers who are sort of... Yes.
Somehow, there's one guy, there's a guy who seems to be the gatekeeper of all medical knowledge on Wikipedia.
Yeah.
And yet, he's a PhD in English.
And he's a retired computer programmer.
Yet this guy is the gatekeeper of all, not just all COVID knowledge, but all medical knowledge, apparently.
That's how Wikipedia works in that way.
People who try to fix it, he immediately then, you know, they basically, anyone who keeps trying to fix the errors and say simply that this was based on an article which I denied and stuff like that, because I put it on my website, he just kept changing it.
And in the end, funnily enough, I'm one of the few people, I haven't looked at it lately, but I'm actually, unlike real big hitters like Brett Weinstein, like Robert Malone, like Peter McCullough, people like that, who are still defamed right up front in their Wikipedia with exactly these types of things, calling them major promoters of misinformation and all this type of thing.
Eventually, unbelievably, that thing on me did get removed.
Here's another interesting thing about Tom Whipple, and I'm happy to say this.
After I put out my tweets about this, saying this was based on the lies in his article, he actually had the audacity to send me An email, a threatening, he says not threatening, but he sent me an email on Sunday saying you must, you must get rid of that, you must remove that, that tweet, excuse me a bit of a liar, within 24 hours.
Right?
Or, you know, and a whole load of other rub, rub, nonsense.
I didn't incidentally, I didn't remove it.
Well done.
I didn't remove it.
But that's the type, that's the sort of people that we're dealing with.
This is the world we're living in.
And you know the thing is, what gets me in it there, he was saying, if you believe I defamed you, sue me, right?
He was saying that.
He knows.
And yet there's a threat that he's going to sue me, right?
Because he knows, he's not taking any risks.
The Sunday Times is going to pay all his litigation costs.
He knows I would never... I've never sued anyone in my life.
It's not the sort of thing I do.
Who's got the money?
Who's got the independent money to go around suing people?
To do these sort of libel cases.
It's ridiculous, you know?
The only people who can do it are people like him, because he's got the Times to back him.
Yeah.
Yeah.
No, I think he's a... he's a... not a good person.
So, did that jeopardise your appearance in this court case?
Were you removed?
So, they did... they used it.
Interestingly enough, the lawyers used it as a... to my advantage in... they presented it.
The lawyer, the plaintiff's lawyer, asked me about it, but only after... but did it in such a way that enabled me to say, this is how Difficult it is!
For the people who are questioning the safety of the vaccines to get their message out.
So it was used as an example of censorship.
Actually, there's a funny thing, another funny thing about this.
I looked recently at the Sunday Times or whatever it was, Times or Sunday Times.
It was a double page spread in which this particular article naming me was on.
And you know what the main headline across this article was?
AstraZeneca vaccine 100% effective and safe or something like that.
That was the headline.
And yet we're the spreaders of misinformation.
Actually, I'm meant to tweet this out, so I want to get that tweet back to Tom Whipple.
Look at what this headline was on that same massive toupee spread you were doing in March 2021.
Because this is the other interesting thing.
What happened to the AstraZeneca vaccine?
This was the great British invention, which is going to bring us, save us, save the world, you know, kind of thing.
And the interesting thing about that vaccine is that as early as July 2020, I knew that they were, although it wasn't withdrawn, I knew that the doctors were no longer, had it available.
You had to, and even if you asked for it, you know, that they had to go through, it was a difficult process to get hold of it, right?
And the reason why, I think now it has, have you heard, I think it might have been, They might now have actually been formally withdrawn, but if it has, it's only very, very recent.
Because they didn't want it to be known that it was withdrawn, they just wanted the story to simply fade away.
They wanted people just to completely forget about it.
Isn't that the... Because we know that AstraZeneca has caused, we know that that has caused not just serious adverse reactions, but deaths.
I mean, all of the ones which have officially been omitted, of course, were AstraZeneca deaths in the UK.
Yeah, I mean it is, I think, was it the AstraZeneca that gave Alex Mitchell his, you know, his Thrombosis and stuff, and blood clots.
Yeah, and it was also the one that the BBC, what was the name of the BBC presenter?
Oh, that's right, yes.
And also Vicky, what's his name, Vicky Spitz's partner, Zion, and Yeah, and a small number of others who've been... But isn't it also... This was the vaccine that was produced by the Oxford Group, wasn't it?
isn't it?
This is the one that got Sarah Gilbert, her standing ovation.
- A little standing ovation at Wimbledon, yeah.
- It's extraordinary, isn't it?
And interestingly enough, extraordinarily, that standing ovation at Wimbledon was after I had already found out that they were no longer giving the vaccine.
That's the thing, though.
Very few people knew that.
Nobody else knew it.
Yeah, I mean, doctors knew it, but... Do you not think, like...
Suppose you got given a Victoria Cross for gallantry, and on your citation it said that you'd done these amazing things, and you'd stormed the enemy machine gun post with just a grenade and your service pistol, and you knew that actually you hadn't done this, and that somebody else had done it, or it was completely made up, the citation.
I think you'd feel so shit you'd want to kill yourself.
I mean, you couldn't live with that shame.
I mean, I think, you know... Or at least you'd own up to it.
At least you'd go public and say, actually, yeah.
It seems to me that a lot of people are...
I mean, whether it's Sarah Gilbert being made a dame and getting a standing ovation at Wimbledon, or simply people like your fellow professors in statistics who are being bigged up, going to conferences, getting on government panels, getting funding, for lying.
For making stuff up.
For not doing their job.
Yeah, I mean, I won't go as far as saying lying, but let's say supporting a narrative which, if they think about it very carefully and apply their obvious skills to, they might come to different conclusions.
Well, that's just because you're a nicer person than me.
I think you know in your heart these bastards are lying and that they should swing for it.
But look, I'd just like to say, well done for doing all you've done.
And I'm very glad that I finally had a professor of statistics on my podcast.
You've given it a bit of mathematical credibility.
So thank you.
Where can people... Thank you for all you've done as well.
I mean, I've been following you for quite a while and I know how difficult it would have been for you.
Well, do you know why it is, Norman?
It's because I'm a total hero.
I didn't get to win the Victoria Cross in World War Two, but... but I'm doing my best.
Anyway, where can people find your research and read your stuff and things like that?
NormanFenton.com Simple as that.
OK, good.
Well, I hope that you find you have an afterlife, a career afterlife, in some lavishly paid job for some kind of awake company which needs people like you to tell the truth, because I think you sound really good.
Thanks, yeah.
I'd employ you if I needed to, you know.
Anyway, so may I remind my beloved viewers and listeners, do, if you're so tempted, support me on Locals, on Subscribestar, on Patreon and on Substack.
I really appreciate your support and thank you very much and thank you for listening and thank you Professor Norman Fenton.
Thank you.
Great.
Oh, by the way, would you mind leaving your computer on until it's fully uploaded?
I think it will pretty much... Oh, yeah.
That's good.
99%.
Yeah, exactly.
See how shit my internet is?
I've got 41%.
Yeah, see, I was about right, because I can... I mean, I'm looking at the screens, and you're kind of like breaking... yours has been breaking up quite a lot.
Yeah, which is why I've got this new platform which records locally, because I know that my internet is... otherwise this would be un... you know... I've had... I've had Podcasts ruined by my tech.
Anyway, I hope to meet you one day.
It's been great talking to you.
Thanks a lot.
Keep fighting the fight.
Yeah, OK.
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