William Briggs builds upon Greg Glassman’s Castro Ranch talk introducing The Broken Science Initiative. Briggs emphasizes that his critique is not aimed at all types of science, but rather at the growing prevalence of broken and bad science. Briggs highlights two especially impactful examples of broken science, namely the COVID “panic” and climate change. Using these examples, Briggs demonstrates how the reliance on flawed models and blind trust in “the science” has lead to severe political and social ramifications for everyone.

Transcript

William Briggs: I wanna talk about broken science, of course, and I want to echo a lot of stuff that Greg said. I didn’t know what Greg was gonna talk about, but a lot of the stuff I’m gonna say meshes in perfectly on the sort of a practical side of things, and I want to, I want to talk about that.

So, I want to talk a little bit about broken science. We’ve all experienced. Now, not all science is broken. Of course it isn’t. We love science. That’s why we’re concerned that it’s broken. So we’re not criticizing all science of all kinds anywhere, nothing like that. But we are criticizing the recent trend and the increase in broken science and, and sort of, ensconcing broken science.

as the science. You’ve heard this many times, follow the science. It’s never follow science, it’s always follow the science. And that’s, that’s concerning And we’ll talk a little bit about that too. Greg gave sort of the philosophy and things like this, and I wanna give some practical, pragmatic reasons to follow the philosophy.

Why having the broken philosophy – leads to these broken science results that we see. And I want to particularly bring it to two areas I think that are gonna influence all of us or have influenced us and will be important to all of us. I mean, there’s lots of areas of science that are important to different people at different times, but there is two of them one of them we all lived through for the past three years.

The, the Covid panic, and I say panic and not pandemic. And the reason for that is very simple. Maybe some of you remember back in 1957, ’58, we had the Asian flu pandemic. Remember that the CDC said about 4 million people died worldwide from that pandemic proportionally speaking, because there was less than half the number of people alive at the time in, ’57, ’58, it was worse than covid. More people were dying per capita of covid, but there was no panic. There was no overreaction. Nothing bad happened all over the world, and very few people can even remember it right now, unless they were involved in it in some way or another. There was no overreaction, but there was for us.

Why? That’s an interesting question we have to ask. And 10 years after the Asian flu, there was another pandemic called the Hong Kong Flu, and similarly about that number of same number of people died. So again, it was worse than covid in a proportional sense. But again, no panic and hardly anybody remembers it, but when COVID hit, all of a sudden there was just enormous panicked reaction.

Now why was that? And that’s a separate question. From all of a sudden, we had all kinds of science that we had to follow. All, sorts of science they just proclaimed. Here was the science. You had to follow “the science”. I’ll give you a few of my favorites. Maybe you guys have your own. My favorites were the science shield, what I call the science shield.

You know, that little piece of like two by two, three by two piece of plexiglass they had between you and the cashier. The covid bug would come out of the cashier’s mouth. It would come this way, and it would hit that plexiglass and die on contact. It wouldn’t go up. It wouldn’t go around. It wouldn’t go left and right because it was science.

Science gave us this plexiglass. And we all had to follow and respect it. And my favorite picture , it was in Taiwan. They also have a CDC same as our CDC, Center for Disease Control. And they panicked over there too. Officially everybody was wearing masks and they were having conferences and they were announcing daily.

COVID cases and oh my God, there were 132 cases today, all that. We only had 122 yesterday. All this stuff, all this availability, measurement, availability, like Greg was saying, we have all these things that we measure. Just the increase in the ability to measure things made us aware of all these things we never knew about before and we’re never panicked about.

But just because we have the ability to measure, now all of a sudden we’re worried and it was on everybody’s lips. Well, anyway, the CDC in Taiwan was having a conference and they had one of the science shields between the two the director and the co-director who were wearing masks and talking about this kind of stuff.

But they were sitting back in chairs and between them, they were just talking to each other with the mask on, but they had, the science shield did nothing. It was just there. It was performative. It was performative science, but we were assured it was the science because scientists , gave this consideration and that was supposed to be enough for us.

So we all had all of this presumption that scientists, when they proclaim something, it must be right because they’re scientists after all. Look, we have jet airplanes, we have you know, we have modern medicine, all this kind of stuff, so they must be right about this stuff. True? Well, no a lot of this stuff didn’t come to be science until they, , they did it backwards.

They said, you know what we should do? We should have these science shields. Now let’s go find evidence that they work. And it’s very easy to do that because I can come up with a model that proclaims that they work, and we’re gonna come back to this. This is very, very extremely crucial. You understand models in science and just what they are.

And just what their limitations are and just how badly they can be abused. Well, what else did we have? We had those one-way lines on the grocery store floors. Remember those? If you’re going north up the alley and another guy comes and he passes you going north, you’re absolutely fine. But if he’s coming, he’s violating the rule.

And I got yelled at for doing this, coming out of a door on Beaver Island. You’re going the wrong way. Coming out of the, he was coming this way. I went this way. Oh my God, he’s probably dead by now. I, I wasn’t wearing a mask, but he was. So, and the masks, that’s another great one. So there was a Spanish flu.

You’ve heard of that back in 1917. That was a, that was a big deal that, that killed a lot of people. A lot of deaths caused by physicians in that one too. By massive overdosing of aspirin and other types of harmful treatments. But, That’s a whole other story. But they, they were in certain locales, like, I think it was St. Louis and, and I think also San Francisco, that they required masks. People don’t remember this now, but there were people that required masks. Some municipalities, not the country as a whole, and not the world as a whole. And the very first studies of effectiveness of masks. Well, do they work? I mean, it seems plausible.

Well, this is, I got, it’s a mask. It -it’s stopping you right. Well, so it must work because it’s a mask. Everybody heard that from everybody. So they studied it. Did it work? No. In 1918, their first studies came out and they were followed by a hundred years of studies Afterwards, they studied masks in every possible context, real world studies, where people wore them and they checked and they say, did it really work?

I wore the number of people who got infected wearing masks different than the number of people who got infected not wearing masks, actual genuine studies like this, there were studies operating rooms. These are all , major studies in, in Britain. The surgeons went without masks for a period of about six months and, in one of their prominent operating rooms, and they looked at infections in the patients before and after the six month period.

No difference. No difference. The reason the surgeons wear masks, Very simple. -You got spit, look at me. These poor guys here are suffering the worst. This stuff’s flying outta my mouth. That’s true. You don’t want to have liquids and fluids and stuff coming out. Particularly the surgeons don’t want it getting in.

So masks make a kind of sense in those, in those situations. But there was study after study, in fact, right at the very beginning of the panic, right when people in America were making mask-lessness, going out without a mask, a crime. Oh, a crime. A crime. This is now illegal to go out without a mask. It just boggles the mind.

There was a study that came out, it was in preparation for quite a while by Xi et al. It called something called a meta-analysis. What they do is they just looked at all these different studies, compiled them together, and they look, does masks have any, do they do anything about stopping respiratory viruses from spreading?

No, nothing. Not even close. And at the very beginning of the panic, they actually did it right? Only there, there, there was one major study in Denmark. They gave people a whole set of people masks, the N-95 masks, and they taught ’em how to use ’em free. They gave it to ’em free, a big supply, and they showed ’em how to use it, told ’em the etiquette, masks them off and all that kind of stuff.

Another half of the population, these were thousands of people, tens of thousands of people went maskeless, and at the end they looked at infection rates in the groups. Any difference? No. No. And so, but people reacted angrily to that. How can that be? Because our scientists say mask work. How did they know mask work?

Aha. We get back to the models again. They created models, particularly with dummies. They would put these dummies, you know, like mannequins and the like. They would put dummies, well, you can read into that word, whatever you like. They would put masks on ’em and they’d inject little things and they would trace the air and look at all the stuff that’s going on.

Oh, masks must work. And now we have to stand six feet apart. Social distancing. Now, where did that one come from? We’re gonna come back. That one has a fascinating backstory. I’m gonna come back to that. But it’s six feet apart. You come with it five, five foot 11, that’s it. You might as well have your wheel made out, but if you’re six feet, one and pass while you’re perfectly safe.

Because scientists have confirmed this through their models, but there were artificial constructs, and that’s what we’re gonna come back to, these artificial constructs. I mean, what, what else happened ?, Did I miss any favorites during the panic of of the science, we were told vaccine, ah, vaccines.

Yes. Well, yes. How could I, how could I have forgotten the vaccines? Yes. Well, What can you say about the vaccines? It became, of course you lose your job. You don’t take the vaccine. I don’t know how many people here lost their job. We were, many people I know were threatened with it. Some people I know did in fact lose their livelihoods because they refused to take this vaccine. You couldn’t criticize it. They told all kind of lies, absolute lies. I’m not talking about even injuries here. I’m talking about the lie that said, if you get this vaccine, the CDC director said, Rochelle Wilensky at the time, a medical doctor, somebody who should know better said, you’re not gonna be infected.

But that’s impossible. Vaccines don’t protect you from infection. They protect you after you get infection. If, if they indeed work, you get infected and then your body fights off the infection. That’s what they’re supposed to do. And vaccines by definition cause harm. If they did not cause harm, they would not work if they did work, right?

They get in there, they get into your body, they infect your body with a a form of the illness, which your body then trains itself to overcome. This is the theory. That’s an injury, that’s harm. And they said, no, no, these things can’t possibly cause harm. Now these are lies. They should know better. I can’t believe medical doctors don’t know this.

This is just not possible. So they went for the big lie and they admitted in various aspects, they went for the big lie to encourage people away from vaccine hesitancy, a new brand new term that they, that they invented vaccine hesitancy. And it was to call those people. Who weren’t following the science to give them some kind of a bad name, and they were scapegoated and excoriated and all this kind of thing.

So that’s, that’s the panic we all lived under. It’s only now gradually dying out, but it’s being replaced by global warming or which they now call climate change, which is a brilliant name. It used to be, it used to be global cooling back in the seventies. You, you remember this. And climatology was not a very big field then.

This is true. There weren’t a lot of scientists talking about this at the time, but there were some, and they were very prominent, big names in weather meteorology and, and geography, which is where a lot of climatology was done back in those days. And they said, well, burning oil, stop me if you’ve heard this one before.

Burning oil was gonna release all these things into the air pollutants. Smog and the like, and it was gonna get up into the atmosphere, create clouds, and the clouds were gonna knock back the sun’s rays and the plunge the earth into another ice age, which was anyway due because of orbital variations in the like.

And this was believed. And then unfortunately for the scientists and their models, the atmosphere did not cooperate. From the forties to the seventies, it was getting generally cooler. And then after that it got gradually warmer and we got global warming. And the theory was that burning oil was gonna release all this stuff in the air that was gonna, instead of creating clouds and knocking the sun back, it was gonna trap the outgoing radiation of the earth and make us warmer.

Well, that worked for a while and then the atmosphere stopped really generally heating, so they decided to call it climate change. Climate change. So what is that that, well, the Earth’s climate, of course, has always changed. It’s never been constant and it’s therefore rational to presume it’s always going to change.

So, but why do they believe we need to, we need to think strongly about this. Why do they believe? Why is all this panic out there? There’s so many people believe in climate change, and if you, you say you don’t believe in climate change, well, you’re a climate denyer. Well, that’s right. Yeah. I deny there’s a climate.

What does that even mean? It doesn’t mean that you deny the, there’s a climate, it doesn’t even mean you deny that there’s a human component to atmospheric change. Of course, there’s a human, there’s a there. The trees are a component to climate change. Everything is a component to climate change. But how important is it?

What can we do about it? So why do they believe what they believe? And we’re gonna have to talk about these models that they use. So again, it comes back to models, it comes back to faith in their own ability as scientists and models. So we need to think about models and the propaganda for this. And it is propaganda.

It’s absolutely relentless. There’s a, a certain school in New Jersey and they boasted about this, I think it was in the Wall Street General or the new or, or the New York Times. I forget which. They boast about this from kindergarten on all the way up through high school, and in every class they teach an aspect of climate change.

They, they purposely do this. Now, I don’t know how they can do this since they can’t possibly know as we’re about to discover, but they do do this. And so you come out of these schools and how do you not get yourself all riled up about climate change? There’s a lot of people now, they, they, they call it climate anxiety.

They invented this new psychological term. People are going to psychologists, they think the world is going to end soon because of a climate crisis and so forth. And the government is using this because of all kinds of interesting reasons we can talk about or not. I don’t wanna talk about the politics of the thing.

I want to talk about the science of the thing and how they know about this. Now, science has, just like we saw in Covid and even more so with climate change. Because they’ve been doing this a lot longer and they’re increasingly doing it now. A sort of a bandwagon effect. Government money is directed and government money is really this.

The scientists themselves deciding what’s important, where it’s going to go, who’s going to get funded, what’s going to be studied, all this kind of a thing, that’s not always a bad thing. But with climate change, people have gone a little nuts. And so I came across this thing with day, I’m gonna read you some headlines I found.

I came across this thing that said a certain area of the world in which I was is warming twice as fast as the rest of the world. And that sounds pretty bad, right? Oh, you know, California is warming twice as fast as the rest of the world. That means it’s really bad out there. We need to do something. So I thought, what other areas of the world are warming twice as fast?

As everywhere else. So I just looked it up. I looked it up, scholar Google, I looked at, these are all papers or headlines about science papers from the last 10 years. So all this stuff is recent. Where is warming faster than everywhere else? The Arctic. The Arctic is warming four times faster than the global average Europe.

Europe is heating up more than twice the global average. Mediterranean Mediterranean region is warming 20% faster than the global average Middle East. Middle East region, heating up twice as fast as the rest of the globe. Africa Africa’s climate has warmed more than the Globe’s averaged since pre-industrial times.

Russia. Russia, warming two and a half times quicker than the global average China. China warming up faster than the rest of the world Report. West Asia countries in Eastern Mediterranean, west Asia are twice as fast as the global average Singapore. Singapore is heating up twice as fast as the rest of the world.

I have, I have documentation on all these things. Japan, India, Pakistan, Indian, ocean, Australia, New Zealand, Southwest Pacific, Antarctic of the Earth’s Lakes, Canada, of course, Latin American, the Caribbean, Mexico, central and South America. The Atlantic Ocean. The Atlantic Ocean is warming the fastest. And finally, I bring us to the sea of the regime itself.

The once United States of America, the United States of America over the past 50 years has warmed 68% faster than the climate as a whole. These are all scientists saying this. These are all scientists, peer reviewed studies, published on models that make all these claims. So I thought, for fun, you guys can do this.

I didn’t finish this exercise because I got a little bored with it, but it turns out I think that every state. Is warming faster than every other state. New England. New England, warming faster than the rest of the planet. Northwest, northeast region, excuse me of the US is warming faster than the rest of the country.

California. California, warming more than the US average as CO2 climbs, great Lakes Basin, Southwest, upper Midwest, Utah. I got bored after this because I didn’t wanna type in all 50 states, but somebody can try this. I was on Twitter doing this, having fun showing this kind of a thing and.

Surprisingly, this does not convince people that the models must be wrong. It is impossible. It is not. It’s not a question of science. It’s not a question of science at this point. It’s a question of logic. It is impossible. Everywhere is heating up faster than everywhere else. That can’t be done, all right?

It’s just not possible. So somebody is making a mistake, and probably everybody is, because it turns out. You’ve seen all these graphs and they correlate with the CO2 and all this kind of a thing. There is no such thing as a global average temperature. It doesn’t exist. What does exist is a model. So think about it.

I mean, there’s the globe is very big and there’s all kind of ups and downs and all this kind of thing, and there’s an ocean. We don’t have thermometers everywhere. And so in history, there’s no thermometers at all except for very few locations. In the world. So what do they do? They have to use proxies and they correlate these proxies, like tree ring data you might have heard, or ice core data and things like this.

They measure these proxies and they say, well, if we put these into a model, these proxies, here’s what the temperature was. Well, now here we get to this difference between parameters and models. This gets a little complicated. Parameters and models and predictive ability because. These models, they’re very complex models, and they all have all these little parameters, these little dials and twists and knobs and switches and things like this you can throw.

And what they end up doing you’re gonna have to take my word for this, for the most part, is they announce the value of one of these parameters. And that becomes, for instance, the temperature. They announced the value of the parameter. , they don’t announce the probability of what the actual temperature was.

They announced the value of what the parameter in the model was. That’s representing temperature. That’s a big difference. It’s a subtle difference, and that’s why it goes missing to most people. They don’t see it and they don’t report it this way. Certainly when it, by the time it gets to the newspapers, it’s completely washed.

Any kind of intellectual content’s washed away, and it becomes pure propaganda. But in reporting on these parameters, there should be at least a plus or minus. We can’t know what the temperature was in 1850 all over the world just because we looked at a couple of tree ring data somewhere. It doesn’t work that way.

There must be a plus and minus, and very occasionally in the journals, the, the scientists know this and so they’ll publish a plus and minus, but of the parameter of the value of the thing inside the model, model based uncertainty. Which is not what we want. We don’t care about the model. We want to know what’s the prediction of reality.

What was it really like? What’s the real plus or minus on what it was really like? Now we talk about predictive ability. Of course, Greg and I are simpatico about that, but how do we even check? We’ve got this prediction of what the temperature was, 1850, 1849, 1800, all the way back. How do we know? How can we check?

Nobody was there. We’re taking a lot of stuff based on faith. We’re, we’re hoping that these models are accurate with no way to check. We’re hoping the models are accurate with no way to check. And they say, like they said all throughout the Covid panic, trust us, we’re scientists. Now, this doesn’t sound like it’s very interesting and it’s a little dull and it’s a little dry.

But it’s these models they terrorize us with and they themselves believe are paramount importance. But that’s why predictive ability becomes the most important thing. Because I don’t care about your model. I don’t care how sophisticated it is or how many degrees you have or how many computers it runs on or anything like this, I want to know.

Exactly how that model makes predictions that I can check myself. If the, if these models are gonna say the temperature in California next year, and you have a thermometer you can check is gonna be this plus or minus this, well now you can check it. You don’t need anybody else to tell you what it’s gonna be.

That’s not what happens. Instead, they have all these weird things and they control the, the data behind all this kind of thing, and they say, here’s this contrived measure that we have. Based on another model, a model based assessment that we have. And we think they’re in agreement and they, there’s never been an independent audit.

I brought this up when we were at Hillsdale. There’s never been an independent audit of these models. You can’t trust them, and it’s not that they’re bad people or anything like this. They’re out to get you or they’re nefarious or, or whatever. But everybody makes mistakes and people can’t see the mistakes that they make.

Especially when they’re that smart. They fall in love with these models. These models become real to them. They love these models more than they love reality often. So you have outside, independent, disinterested people doing this kind of stuff. And that’s exactly what we need in science too. It’s just to show the bandwagon effect.

I, we could do this later for fun, for people who wanna see it. I was interested not only you know, and where is everybody warming, but you hear all the time, this thing is going to be exacerbated by climate change. Prostitution’s gonna increase, because of climate change. you know, that child trafficking is gonna increase because of climate change.

Corn production is gonna be down because of climate. Everything is going to get worse because of climate change. And these are all based on scientific studies, which is to say based on models cuz they don’t know, how can they know? Climate change hasn’t struck yet. So I did. It was fun too. This is something else I did on Twitter, and if we have, I, I think I’ve gone on about this subject as long as you guys can tolerate it, but you go to scholar.google.com, that’s like Google for scientific papers and you just type in whatever word you want, like wart and in quotes, climate change.

And there’s gonna be at least one paper on that thing. I challenge you to find something that isn’t, and I thought we’d do a little back and forth, but I did. I I tried everything. I did. Pizza. Yeah. Pizza’s going to, either it contributes to climate change or it’s gonna be affected by climate change in a negative way.

Anything good is gonna be damaged by climate change. Anything bad like weeds and poisonous snakes and spiders, those things are gonna absolutely thrive under climate change. Anything that we love and is photogenic and is delicious, that’s gonna be absolutely harmed by climate change. This can’t be alright.

It’s logic. This one is at least logically possible. It’s possible that a slight average increase of a 10th or two degree in the average model temperature of the world will lead to nothing but Ill. To everything we know and love. That’s logically possible, but it can’t be believed, but scientists believe it.

So why, again, we have to talk about models. So let’s come back to this social distancing model. How did that come all of a sudden the, this was a brand new invention. Oh, we need to social distance it, it hit us like this overnight. We need to social distance. Well it has an interesting story. There was, it was a, here in California, as a matter of fact Ooh, if memory serves, maybe I’m wrong about that.

This is the thing, our side, when we make these claims, we get one little thing wrong. Oh, she turns out to be from Nevada. Well, now Briggs entire speech is invalidated because I got the wrong, I forgot to dot the wrong i, so don’t hold me to the state. But I think it was California. This girl high school project, she thought, you know what, let’s she was interested in in epidemics.

She was interested in epidemics. And so she had , her father was a scientist at one of the national labs. And so she thought it would be fun to use this social agent model, a social agent model. What is that? Well, it’s a computer that’s just pro programmed to have these fictional agents who move her on and like a video game, like fashion.

And so she programmed the computer to say if. These social agents come within six feet of each other. The infection rate is going to be high. They’re gonna be able to transmit a disease. The the disease transmission rate’s going to be high, and if they move past six feet, the disease transmission rate’s going to be low.

All right, everybody following me with this? Okay. So she ran the model, and what did the model discover? The model discovered that if everybody stayed within, if everybody got within six feet of each other, the disease transmission rate was high, and if they went beyond six feet, the disease transmission rate was low.

Do we see a problem? That problem is the same problem of all scientific models. All models of any kind of any kind in every field. Only say what they’re told to say. All models only say what they’re told to say. If you can remember nothing else from my talk, remember that all models only say what they’re told to say.

They’re just on a dumb computer. Computers can’t think. It doesn’t matter. AI is nonsense. That’s another whole other thing. That’s another model. Ai, artificial intelligence, that’s the stuff that is used by people who are buying AI propaganda. Everybody’s coming out in business now. You can either buy, you need to use our software, which is AI powered.

These are just models. They could be good statistical models, they could be poor, I don’t know. But it doesn’t mean that the computer is thinking, the computer is just telling, is doing what it is told to do. You give it a set of instructions if this, then that, that’s all it does. This little girl’s model was used as her father then got got with her and helped her on her science project.

The father still do that. Helped her kids on science projects and they wrote it up into a paper. And that paper was noticed by the CDC and the CDC then called it the science, but that paper only said what it was told to say. There was no independent verification of it. But the interesting thing about it was as soon as that paper became out and we all told that six feet was the magic distance, you remember you had to stand.

Here’s we’re standing in line at the atm. I don’t know if this is six feet or not. The guy is standing right here, right? And, and I’m standing right here. Here’s the ATM number two. That guy’s standing here. This guy’s standing next to me here. That’s okay. Because the virus can’t think laterally. You can only think backward and forward.

The same thing happened at our church when they finally allowed ’em to reopen. The pews were only every other because they wouldn’t allow you to sit sit, but they could sit as many as you want. So more science. This happened time and again, but yet scientists were able to go, other scientists were able to go and validate that original model, how?

By creating their own models. That said the same thing. Minnesota looked at this, the governor, he said, you know what this model tells us? It’s very important that if we, if we locked down for another two weeks and we socially distanced for this, we’re gonna be able to save such and such number of lives.

These were, these are absolute predictions we could test, and none of them worked. They were all wrong, but they were believed. They were believed because the computer said it. There’s still this alert. You think by now we would have, we would lose this mysticism about computers. Like there are somehow wonderful things that can tell us that we what we don’t know, and we should trust them.

Like it’s like out of a science fiction movie? No, it’s the people behind the computers. We need to look at the people telling it what to say. That’s the absolute difference between all these things and models. It’s, it’s not that you can’t use model. Of course we need models. There’s no other way to make predictions.

Models can be good or bad. It’s not that because a model only says what it’s told to say, it means it’s a bad model. That’s not what I’m implying at all. But if you’re saying that the model. Discovers that social distancing is a wonderful thing. No, the model had that in there as the premise. You can’t use that premise to say that you then, this is the conclusion That’s circular arguments and that’s became those circular arguments became the basis of legal decisions.

You, you can’t now, right? You can’t wear ’em. You can’t go out without a mask. You must lock down lockdowns. That’s another bit of science. I, I’ve said this a million times, I get sick of saying it myself Every year in the United States the deaths increase in January all cause deaths. Deaths from any, anything.

They peak in January around January 15th or so, you know, plus or minus, and then they bottom ’em out in the summer and they swing back up in the fall and they peak in the winter, and then they bottom ’em out in the summer and they peak up in the fall like clockwork. This is one of the oldest, if you want to call it public health observations that we know of. And why is that? Well, in the wintertime, it’s cold. We all go inside, share our germs and disease from all causes increase. You, you get one thing. This is a whole other thing we could talk about about causes of death. That’s actually a very complex topic, but what did our government decide to do?

Lock people down. Lock people. Everybody get inside. If you’re non-essential, get inside. The worst thing they could have done, the absolute worst thing they could have done, and there was no talking them out of it because it became the science and you’re always able to go back and create even more complicated science and yet another model that confirms the science that came before.

But unless you actually validate that science by making predictions, anybody can check, we should not be believing any of this stuff. I think I hit everything I wanted to talk about that. Oh yes, prediction is extremely important and, and, and absolutely necessary.

But it’s not sufficient because we still need to understand cause. Because if we’re not attacking, cause then what we do to try to change things might be wrong. And I’ll give you a very simple example about about that. We, everybody remembers the Ptolemaic kind of universe the epi cycles predicting why the planets move and the fashion they do and this kind of a thing.

They had a wonderful theory of these cycles and epicycles and the movements of, of all the planetary motion and everything. It was a wonderful predictive model. It made excellent predictions, but that didn’t mean that the, the cause, these epi cycles and everything were real. They were not the cause. And if we were up there and we wanted to change, suppose we were some advanced galactic civilization, we wanted to change the planetary motions or orbit of some of these planets or asteroids or something by creating a miniature black hole or whatever, we would be doing the wrong thing because we identified the wrong cause.

That was not a causal model, although it made excellent predictions. So we really need to attack cause and we need to understand what cause is and the limitations of cause and all this kind of a thing. And all this stuff is extremely important. I’m working with some people now in the Netherlands. The crisis there is not carbon dioxide, but nitrogen.

And they claim nitrogen is doing this pretty much the same thing that carbon Dioxide is doing here. No, it’s just not true. How, why do they believe this though? The same story. They build these models. They have, wee p values, they have all this kind of stuff that they blow up from these very small things into a major policy thing that the government could latch onto.

They’re kicking people, they’re gonna kick people off their farms. In Ireland, they wanna stop, 200,000 head of cattle because they believe the earth is imperiled by all this kind of stuff. People are losing their livelihoods because of these models that do not validate.

And so we think, ah, it’s nothing here. But look what happened to France. They’re already proposing no short haul flights to save the planet because of carbon dioxide. What’s gonna happen here? They’re brooding about these so-called 15 minute cities. They really hate cars. They don’t like the freedom in cars.

And they want to stop people driving because of how that affects the planet. There’s political reasons for all this kind of stuff here, but they’re always able to find, because there’s so many scientists now, they’re always able to find scientists who can build models that will validate the beliefs that they have.

That’s why it becomes ever more crucial, even if you, we don’t understand the cause that we have to have this predictive ability. We need to understand. How predictably accurate these models are and they need to be done in an independent way. Well I think that’s about it. That’s the same idea.

There’s lots and lots of stuff we could talk about if anybody wants to talk about after. I, I found it fascinating. Greg’s slide of all the, you know, the necromancy and the, and the screen and all this kind of stuff, why they believed that they had predictive accuracy, these people, otherwise they wouldn’t have used it for so long.

So how did they come to this belief? Albert Schweitzer was in Africa, and taboo was very big people taboo, you know, forbidden things to do. Taboo was not just a cultural thing, but it was very personal people. Same thing happened in here with the Comanche and Cheyenne and things like this.

Taboos were created by people for themselves, individuals for themselves. And one boy had conceived the notion that if he ate plantains, he would die. And so some classmates of his told him, that , the soup he was eating was cooked in a vessel that had previously had plantains in it. And he was a medical doctor.

The boy complained of this, and he very shortly, within a day or something, died. He died of mental distress by this. Well, that’s terrible, right? But he had conceived this taboo for himself. The the American medical mind will say, oh, you know what it really is. Ah, he had an allergy to plantains. That’s what it is.

The plantains got in there and they monkeyed with his with his digestive system in some way, and that killed him. The problem is the boys were lying. There was no plantains in the vessel, so the taboo killed him. That’s the nocebo effect. We’ve heard of the placebo effect where you give somebody a sugar pill and they improve.

Aha. Why? So we really need to understand cause and medicine and the nocebo effect is just as real. And it was incredible. You think, oh, that’s just Africans in, in a, in a time in which you know, Western technology and science hadn’t spread. And so they weren’t as up to date as we were. No. What happened when someone was wearing a mask and here come you, we were walking down, you’re walking, they’re, they’re wearing a mask and you’re not wearing one.

What happened? Boom, the nocebo effect hits. You broke the taboo. And I knew a guy, Chinese guy of course, Asians, east Asians love masks and they’ve been wearing ’em for years. He got sick with Covid and , I said, well, how do you have confidence? You were wearing a mask? , and he didn’t blame it on somebody breaking the taboo, but he said, well, if I wasn’t wearing the mask, I would’ve got sicker.

You can find the answer in a prediction if you look hard enough. That’s why you have to have outside control, again, rigorous auditing of these things with pre-specified unambiguous endpoints that everybody agrees upon beforehand. cuz I, I’ve done testing psychic testing.

I’m an amateur mental magician and I do tricks and things like this. And I’ve done these, these kinds of tests before on these kids who believe that they, I’m not gonna go into the whole story cuz it’s long and too, too long. They believe they could see inside envelopes the color of cards using a form of like a mental telepathy, but they called it brain respiration. A heightened sensory perception was their phrase for it. So I designed a test and we went up to MIT and did it out with this guy. You heard of Don Yoga. You guys have seen that that’s, I guess it was more of a fad had died out a little bit. It was big 10 years ago.

But the idea is that even these people when they fail, they still found reasons why they succeeded. And it’s the same thing in science. Even when these models fail, they can still find a way that they can claim success. All right? It’s always possible to do that. That’s why everything has to be known in advance, in a very rigorously defined, I think that’s about it.

We can talk on and on about all these kind of things, especially about if you’re giving somebody a drug and you think the drug has a real active component to it, real bioactive component to it. How much of that bioactive component to the success of the cure, if there was a success, is due to the drug?

And how much do the placebo effect? We know the placebo effect is real. Everybody admits it. So how much of that is due to the drug and how much the placebo, how much is due to the nocebo effect for people who aren’t getting the drug? All these kinds of things. So cause is extra extraordinarily important than we neglected.

And that’s my speech. Thank you very much for listening. I’ll take any questions.tb