Housekeeping Note :
First off, I must apologise for getting behind (yet again) with replying to comments. For some reason I’m just really rubbish at this. I’m trying to fix myself, but in the meantime I am really grateful for all of the comments and I do read them all.
Another thing I’m really grateful for are the pledges of support some of you have made. I am truly honoured by this. I am in two minds about whether to turn on paid subscriptions or not. I will never put material that’s only for paid subscribers on here, or restrict comments to only paid subscribers. These are things that I really hate on other Substacks (although I totally understand it).
I could definitely do with a financial boost, however modest, but I can’t in good conscience expect people to pay for my ramblings. Even the mostly knockabout stuff I often write takes time, but I’m OK with spending that time and getting nothing back (financially). However, many writers on Substack do put in a lot of effort and I agree there should be some financial reward for doing so (I just can’t afford to subscribe to all of the valuable stacks I read). I also really appreciate what the Substack platform has achieved and it’s a good idea that we support this, one of the last few remaining strongholds of free speech we have left.
And so, on with the rambling for today.
One of the things covid has really brought home is our need to better understand technical arguments. We’ve all had to get that little bit better at interpreting graphs, understanding sources of bias, trying to properly assess risk, and so on.
We’ve had to get pretty good at recognising when our governments are “trying to pull a fast one1”.
It’s obvious the duplicitous fuckers are trying to pull a fast one when they spout clearly deranged propaganda like “nobody is safe until everybody is safe”, but a lot of the time their deceit comes wrapped in a glittery and shiny covering of Science™.
Much of the shiny stuff that hides the Great Smelly Turd of Implausibility, is created from a wholly misleading abuse of statistical arguments.
But get this, it’s exactly what they accuse us of doing. In fact we2 got so bloody good at this kind of thing - creating arguments against the vax from the published data - that they had to stop publishing the data because it was being Misused™ by us awful anti-science types.
It was a case of : we’ve got the data and it shows the vaccines are just ticketyboo with more ticketies than we know what to do with. You can’t see the data, though. You’re just going to have to trust us.
It’s still like this. There is a very persistent stubbornness in refusing to reveal the actual full datasets that could settle the question of vaccine efficacy3 once and for all. There must be some reason they’re not publishing the data. Can’t think what that could possibly be.
Then, with the limited datasets they did reveal, they’d throw in all sorts of caveats about interpreting the data with things like age-adjustment and healthy vaccinee effect. Sufficiently sciency sounding stuff that is designed to sound plausible4.
My main recollection of my personal state, other than rage, during the height of covid was that of incredulity. It wasn’t just the crazy and misleading ways the “data” was being used, but the whole process surrounding things. I couldn’t believe the absolute perversion of the scientific method that I was witnessing on an almost daily basis.
For example, the whole “death within 28 days of a positive covid test” being classified as a covid death just appalled me. It was a repellent sleight-of-hand deliberately employed to make covid seem much worse than it was. It made an absolute mockery of any proper scientific attempts to understand the scale of the problem (but it was jolly good for The Science™).
Another example. Without any justification, or any new evidence, or any real discussion, lockdowns became The Science™. It was an experiment in the classic sense. It was trying something new that had no prior evidence base and went against all previous scientific advice on what to do in the event of a pandemic (i.e. against decades of established evidence and pandemic preparedness planning).
That’s kind of OK5 if doing an experiment (Mengele style) is what you’re trying to do - but that wasn’t the way it was “sold” to us. It was presented as if this was the accepted scientific wisdom - when it was quite the opposite.
All of sudden, you were a science denier, a fringe lunatic, a granny killer, if you questioned this newly “settled” Science™. Millions of people bought into the lie. Just like they did with masks - another Scientific™ reversal of everything we previously knew to be true.
My problem with much of Science™ - on things like covid, or “climate change” - is not so much with the specific ideas but the unwarranted certainty that is expressed. It’s a certainty that is not only wholly unwarranted, but one that is actively heavily promoted by official and media sources.
Some things in science (not Science™) we can be reasonably certain about. We know, for example, that for the vast majority of everyday things Newton’s Laws work pretty damn well. But when it comes to things we don’t know everything about - things like the complexities of the climate or how viruses are transmitted - we need to be much better at recognising the provisional nature of much of our “knowledge” and we need to be far less certain about the conclusions we draw.
Science does not provide absolute certainty; mathematics does.
We do not “prove” things in science, except in a negative sense. We can, for example, do an experiment to prove that some given hypothesis is incorrect, but we cannot prove that a given hypothesis is correct.
For the UK data during the first wave of covid I can actually prove that the hypothesis “the UK lockdown caused the death curve to go from increasing to decreasing” is incorrect. It is possible6 that the self-imposed restriction on movement of the UK population prior to formal lockdown (as evidenced by Google mobility data) had some impact and was enough to alter the disease dynamics - but the formal ‘official’ lockdown did not, because by this point the ‘turnover’ of the curve had become inevitable.
The thing here is that a strict Wuhan Welder approach to lockdown ought to work. If you literally weld everyone into their houses for a month then you’re almost certainly going to reduce the spread of a disease that is transmitted by an airborne virus. The problem here is that, realistically, even in places like China, this can’t be done with 100% coverage. What is the impact of 50% coverage of such a brutal policy?
Here, most people tend to be a bit “linear” in their thinking. If you lock up 50% of your population, you’ll reduce the impact by 50% sort of thing. The reality will be more complex and likely subject to some kind of threshold effect whereby there’s a certain percentage “coverage” where the impact takes off, so to speak. Below this threshold you may as well do nothing as it has little significant effect.
I’m assuming that such a brutal lockdown ought to work. The thing here is that I don’t actually know.
More to the point, neither does anyone else. The “professional” disease modellers got it so wrong they might as well have been guessing. Part of the problem are the uncertainties that exist in how respiratory diseases propagate in a population. We don’t have all the answers to that question.
We know that diseases are transmitted from hosts to others - various experiments (and observations) over the years have amply demonstrated this - but the exact mechanisms by which this occurs and why it sometimes doesn’t happen as we expect are not things we have a complete understanding of, as yet.
It would seem, taking a cynical view, that probability has almost been designed to be a perfect tool of confusion7. But probability is the right mathematical tool with which to approach uncertainty.
I’m almost at the point where I think politicians (and scientists) should be fined something like 10% of their assets whenever they express more certainty than is warranted.
I know, I know - it’s a dumb idea that wouldn’t work and could also be misused to stifle genuine debate - but I kind of feel we need to do something to rein back on the kind of egregious certainty we’ve witnessed - especially when it comes to Science™.
We need to go back to the time before education became all about activism, gender and butt plugs.
Even then we have a problem because probability is a real bugger to understand properly. It can trip up even the most stunning and brilliant mathematicians who have ever lived8.
The only realistic way out is (surprise, surprise) free speech. This is not something that existed during covid. Counter-arguments to the official narrative were vigorously culled from social media and those presenting such counter-arguments were outrageously smeared and demonized.
It was all done, like it is with Climate Science™, to create an illusion of consensus.
But science isn’t about consensus, anyway.
We’ve seen notable “intellectuals” such as Sam (dead babies in the basement) Harris and Neil (today I’m 20% woman) deGrasse Tyson argue that it is, in fact, about consensus. But we can ignore both of these ignoramuses9. Neither of them would appear to know what science is about even if it kicked them in the gonads.
Statistical arguments can be very powerful - but they can also be hugely misleading. They’re a great way for dishonest people to delude others. But even without any deliberate attempt at delusion they’re fraught with danger - simply because it’s easy to get it all wrong.
I should know - I’ve gotten them (embarrassingly) wrong far more times than I would care to admit.
As a general rule of thumb I would suggest that when a politician uses statistics they’re probably wrong (in the interpretation or spin they’re putting on them); when a professional statistician uses statistics they’re probably not as wrong.
I don’t know what the answer is. We’ve lived through a period of Rule by Expert™ and it was a complete and utter shitshow; they got nearly everything wrong. Nearly everything they told us about covid, or what to do about it, was a steaming great pile of horseshit.
It really is that bleak.
It’s not that experts (in general) suddenly lost the plot - they didn’t. But all we were allowed to see was the Right™ kind of Expert™. This gave the impression of a consensus that simply didn’t exist. Only papers that agreed with this illusory consensus were allowed to be published, which further reinforced the fake consensus.
I was stunned the other day when a former university colleague (a scientist, no less) said to me that the “vaccine” had worked because the 2nd time he caught covid (after being vaxxed) it wasn’t as bad as the first time (unvaccinated). This is a very, very smart guy in lots of other respects - but he, too, had fallen prey to the propaganda and the fake certainty it created.
Once again, as we so very often do, we come back full circle to free speech. We now know (with a very high degree of certainty) that pretty much everything that was done to ‘combat’ covid was useless. For some of these things, like the ludicrous plastic screens that popped up everywhere, we didn’t need any Expert™ study to tell us how bloody silly it was.
But the terrible, terrible policies and mistakes10 that were made were only possible because speech had become heavily curated - it wasn’t free at all. The illusion of consensus, the fake certainty, were responsible for allowing all of this to happen.
You can wrap a turd in all the glitter you like - it’s still a turd.
This is an informal phrase which means to attempt to deceive.
Well, not me personally, because my data analysis skills are not up to scratch, but you get my meaning here. The “we” here refers to all of us who followed the ‘alternative’ analyses of the data and thought it made more sense than the ‘official’ interpretations.
Not to mention safety
And these things are plausible to some extent - but we can only properly assess whether such claims are important (i.e. have a significant impact on the results) when working with the full datasets. We need the receipts, goes the well-worn phrase.
OK in a scientific sense, but far from OK in any moral sense
Possible, but far from proven
Lies, damned lies, and statistics - and all that
Paul Erdős, widely recognised to have been one the best mathematicians in the world, ever, got the Monty Hall probability problem wrong
That’s a bit unfair - but certainly some of their recent statements have been alarmingly dumb
Mistakes or deliberate attempts to wrest more control over society in the future?
If you can collect correct data, but don't - that means you do not want to know what you suspect to be true.
If bringing the boss bad news means self-sabotaging your career, the boss will only ever get shining progress reports.
Combine the two and you get 50% of the reason communism fails every time (the other 50% has to do with how economy in its original etymological sense works in reality).
Sadly, it is not unique to that -ism.
Even if I only did basic stats, I still remember a lecturer explaining the difference between getting the client the numbers they want, and the real numbers.
That difference being, "Do you want a job tomorrow too?"
The biggest problem is that we laypeople do, actually, have to “trust the science” when it comes to many things. I have to trust that there is a difference between the priest and the physicist— different methods, different process, different tools, different goals. Science is the revolution; it should not require a modern day Martin Luther posting theses on the wall. And yet, and yet: child sacrifice seems to be our holiest sacrament (at least in my country) and the people who God tasked with the chore of warning us all against the folly of making idols— his chosen, his beloved— are again our hated enemy. There is nothing new under the sun.
(You don’t have to comment on my comments, RR, I say pretty much the same thing every time maybe using different words if I am lucky.)