around, we'll do that last.

So there's some people speculating that Russia is going to mass forces in Belarus and then attack Kiev from there and destroy Kiev and declare a victory or something. Does that sound like something that might happen? Do you think that's going to happen? That feels unlikely to me. Yeah I'm not going to rule it out. I think that given the winter pause it changes everything because you don't know which side did a better job of preparing for after the pause. So I think at this point the war is a toss-up. I don't think as of today you could say Ukraine or Russia are winning. It looks like a toss-up to me. What do you think?

I think it's now in solidly unpredictable territory. Not that it was ever super predictable. Admit defeat you mean? That Ukraine will lose? I'm always open to that. I've predicted that Russia would be unsuccessful conquering Ukraine. But if they wait long enough I would acknowledge that if Russia wanted to risk everything and wait long enough that they could take Ukraine probably yes. I'll give you that.

All right. It's a draw. I think the most likely outcome is a draw with forever fighting in some of those recently captured areas. That's what I'm guessing.

So there's a new study that says that vitamin D deficiency was found in 82 percent of COVID cases but the general population is only 47. So the vitamin D deficiency was twice as profound in the people who got COVID and probably the outcomes as well.

Now I would like to do a competition and I did a little Googling and I couldn't determine. I feel like I am the first person in the world to say in public. I'm probably not. So this is where you get to debunk me. So if you like to embarrass me this is your opportunity. I believe I'm the first person in the world who said that the country differences and the demographic differences like black people having worse time with COVID, that that was a vitamin D pattern.

Now hold on, hold on. Let me be specific. I'm not the first person to say vitamin D was important to outcomes. Hear that clearly. I'm definitely not the first person or even close to say that vitamin D would help you recover. Would you agree that everybody said that? Like doctors said it, lay people said it early on, like right away everybody said more vitamin D. So that part I'm not claiming anything. I was just one of a million people who said the obvious, hey vitamin D is good for you. All right. So I'm not taking any credit for that. I'm being very specific.

I believe I'm the first person who said it's the reason that you're getting different outcomes in different countries and different outcomes across black versus white etc. I believe I'm the first. And here's why. Because when it occurred to me I'd never heard it. So the only thing I can know for sure is that when it occurred to me I hadn't heard it yet. But it might be out there. So if somebody has a source. Because once I found out that Sweden routinely gives people vitamin D supplements and cod oil or something, I don't know. Once I realized that the reason that Sweden was an exception is that they had a lot of vitamin D. And then if you ever figured out why Africa never had a problem, is that wild? That Africa basically escaped the pandemic without doing much of anything. Do you know what the vitamin D rate is in Africa? Really good because they spend a lot of time outside in the sun. It's really good. So even though they're mostly black so they have tougher time absorbing on average, they spend so much time outdoors that they have good vitamin D.

Now Africa is also young and thin, right? Some people said oh they have lots of ivermectin but it's probably can't be more than 10 percent of the population. I've removed in probably less than 10 percent. I would imagine that most people in Africa are on any kind of pharmaceutical drug at all. And maybe 10 for hydroxychloroquine and stuff. But those things are definitely factors or could be. But the one that just jumped out was that every group that had low vitamin D were the ones everybody said was getting hit the hardest. So I put that out there that nobody made the country connection before I did. Do you want anybody want to fact check that? Yeah find somebody who said in public, anybody who had a YouTube or a tweet or something that the country difference. And I know you're just going to send me people who said vitamin D is good. Do not send me anything that just said I said vitamin D is good. Stipulated. I get that. We all agree with that. Just the country difference. I think that was first. I think I was first in the world actually but could be wrong.

All right. I used to work in big corporations and if you've worked in a big corporation you know there's a process that they like to do in the big company. If something doesn't work, you make a big mistake, a bad product or whatever, your project fails, they like to do a postmortem to find out what they learned, right? So I would like to find out how so many of you got the right answer about the vaccinations whereas I didn't. So I'm going to try to figure out and maybe you can help me figure out what heuristics, what rules of thumb, what sort of common sense but also data did you look at.

And one of the things that people most complained to me when I said that I was giving the win to the unvaxxed people. So I gave them the win because they definitely got to the right place and I'd like to learn how they did that because I got to the wrong place. Because now I have a vaccination in me but I'm not worried about COVID so now I just have to worry about the vaccination, right? So I lost. Right?

So having said that and be very clear that the winners are the people who use some different system to analyze stuff. I wanted to find out what I did wrong. Now I made the mistake of saying all the data is unreliable. So I didn't believe the data from the official sources but I also didn't believe the data from anybody who has disagreed with the official sources. And so to me it was a data-free environment. But it was a data-free environment for many of you. But a lot of people said no Scott you're wrong there was a lot of good data. And I've been sending, a number of people said they've been sending it to me and I've been ignoring it. So let's fix that, right? Wouldn't you say that was a big mistake on my part? That there was in fact good data. It was presented to me. I didn't have to go look for it. And then I discounted it and then I made the wrong decision because I discounted it. Would you agree that's an accurate statement?

So we're going to try to correct whatever the hell is wrong with me with your help. And there will be a little whiteboard here. So I asked people, I asked people how they did it on Twitter this morning. Like how did you do it? Like how did you know? What was the good data and what was the bad data? I'll give you some answers. And then you can add some answers if anything is left out, right?

So here are all my problems, the things I didn't do right. See James pop. These are people on Twitter who responded. I said for me it was just pragmatic. How could something so new, talk about the vaccination, how could something so new be remotely ready for human use in such a short time? Good. That's a good starting place isn't it? Would you agree that that's a good solid common sense? No dispute about the facts. There's nobody who's going to question that the vaccination was rushed relative to what we would all expect or like, right? Good point.

And yeah sorry I like that point. So I responded, I have a lot of respect. This I tweeted back. I have a lot of respect for the people who were smart enough to avoid a hastily prepared vaccination. Is that fair? I do have a lot of respect for people who were smart enough to avoid a hastily prepared vaccination in favor of a relatively the relative safety of an engineered bioweapon that escaped from the lab. So the people who were smart enough not to take the hastily prepared vaccination and rather go to the relative safety of an unknown bioengineered weapon made the right call. And so I'm trying to learn from them.

All right. I put too much weight on an unknown bioengineered virus that looked like it might have been a weapon of mass destruction with God knows what kind of future impact. But I put a little too much emphasis on that whereas I should have been focusing more on the fact that the, and I knew this, I knew the vaccination was not tested as much as others.

All right. So then there were people who sent me good data and I kept saying but how did I know it was good? You see the problem. Because people were saying I have the good data and I sent it to you and I saw it in many cases. I saw it. But how was I supposed to determine that that was the good data? And so I asked people how did you know? Because I couldn't tell. To me it all looked sketchy. Like it all looked bad. I didn't see any good data. Nothing that I trusted. But other people did and they say they could sort out the good data from the bad. So I was asking how they did that.

And here's some more answers on that. Here's how some people did it. Some people said you should trust the people who were banned. That's like a flag of credibility. That the people who are banned were more credible than the people who are not banned. And you know I'm not trying to live in the past. I'm trying to prepare for the next situation. So I wondered if that's a standard we could always use. Was that a standard that only worked for that one pandemic or would it be true for the next one?

And I'm not aware of all the people who got banned but is the argument that everybody who got banned had the right answer? Was there anybody who got banned who had the wrong answer? Or was it only it was banning like a really good signal for the right person? What do you say? Well if you had to put a percentage on it is, and let's say for the next situation, forget about the pandemic, for the next situation whatever that is, should I say if they're banned you're like 80 percent likely to be right and the people who are saying things that don't get them banned are most likely to be wrong or lie about 80 percent? What would you say? Well 25 doesn't help me because if they're only 20, 25 percent likely why are you believing them? I mean it's got to be over it's got like 80, 90 right.

All right. So we're getting some 80s and some 90s. All right good. So I'm seeing some agreement that you can see what data has been banned and also what people. So that falls for people as well, right? The people who were banned were very much most likely to be right and you could tell because they were banned. Is that right? You could tell they're the good ones because they're banned, right? Okay so I'll look more to banned people next time as opposed to people who say things that don't get them banned.

And then other people said they're just some people you can trust more. It's more about trusting the right people. So you know I acknowledge my weakness that I can't tell the difference between good data and bad because it all looks bad to me. So if I can't tell what data is good then I could rely on what people are good, right? Because they're reliable people are most likely to have the reliable data. That makes sense.

So the people who were banned and ostracized, the Alex Berensons and Brett Weinstein and let's see a fat Emperor and Bori something and some other people. So those are the ones we trusted. So rather than trusting the medical professionals a lot of people said I'm going to trust a journalist. Now does that always work or did it only just work on this pandemic? So the people who trust a journalist, Berenson, over let's say the bulk of the medical community. And we know Berenson got the right answer so obviously he knows something they didn't.

But here's the question. Will the journalist who disagrees with the medical community generally be right or only if they're also banned? Are they only right if they're banned? So what if it's a journalist who disagrees with the mainstream but is not banned? Are they still more likely to be right? And what if there are two journalists and one says the mainstream is wrong and one says it's right? Because two journalists would be roughly equal in credibility, right? So how would you sort that out? Because you have one journalist who's on one side but another journalist who's on another. And since journalists are better than medical professionals at medical stuff so we could ignore the medical people but which of the journalists would I pick? The banned one, right? The one. So somebody like a Doctor Drew who did not get banned I should sort of minimize his influence and look for the people who are the most banned to get my credibility. Does that make sense? I'm looking for your guidance.

All right here's some other things that people said. Some people said that the data that they were hiding tells you everything you need to know. And I thought oh that's pretty insightful. Because once you can tell that somebody's hiding some data or that there's a lack of data, the lack of data tells you more than the actual data. You good with that? Does that make sense? The data that you don't have is telling you more than the data you have, right? I mean that's a pretty big signal if they're going to hide that data.

So I'm wondering in general again because I'm not living in the past, trying to prepare for the next one. In general should I use the lack of data to make my decisions versus the data? Should I go with the non-information to base my decisions on?

All right. Then somebody was criticizing me saying that I gave you two pretty clear examples of how to do it. One, you want well credentialed peer-reviewed people prior to COVID. So do you agree with that? You should believe the people who are well credentialed and peer-reviewed even before COVID. That makes sense doesn't it? Believe the people because like Dr. McCullough, Dr. Malone, peer-reviewed, very credible. So you should believe them.

But what happens if the people on the other side are also credentialed and peer-reviewed? Do I, so if they're all credentialed and peer-reviewed but they have different opinions should I take the ones that are in the extreme minority because the minority is usually right on medical stuff? Or should I use that as the base and just say all right but which one's more banned? So if they have equal credentials I should favor the one that's banned, right, next time?

All right. Also the people who risked livelihood basically. They put their careers on the line. So I should trust the people who knew they were going to lose money in this, right? Because they don't have a financial incentive. So that would be like Dr. McCullough, Dr. Malone because they risked their careers didn't they? So that does make you more credible if you're willing to put some skin in the game.

So Dr. McCullough for example definitely put some risk on his medical future. We can only hope that he makes his money back on his book because he did do a book, probably does some speaking tours. So Dr. Malone has a book and Brett Weinstein has a podcast I think it's monetized. But you don't want to look at any of those people because they put their entire financial futures on the line and all they got out of it was best-selling books and top rated podcasts that are monetized. So that's a group you want to believe in because they're not influenced by money in any way. So that was a good idea.

Then also all right what else. This is important. You should not trust any study where the people running the study were funded by big pharma or somebody wants to make money from it. You'd agree with that right? You can't trust any study that somebody's got a money motivation.

But here's my issue and I know you can do this because you did it but I did it wrong so you're gonna have to teach me how to do this. There are some studies where you can tell who funded it and so I would be with you and say oh if it was funded by those people you can't trust it. But what happens if it's a study where you don't know if it was funded? Or here's the tough part. What if let's say there's a doctor who's the head person of the study and that doctor did not get money from any big pharma but the doctor does a lot of speaking engagements for big pharma. And let's say somebody who's close to the decision that we're trying to make, would you know that? Would you know if a doctor who led a study that was not funded by big pharma but would you know if the doctor individually and privately gave highly paid talks as seminars paid for by the same company that he's doing the research for? Would you know that? I don't think you would. I mean I wouldn't. So you have to teach me how you know that because I wouldn't.

It's my understanding that everybody's working for money but apparently some people are not.

All right so I put it all together on a whiteboard so I could try to understand how you made the decision correctly. It looks like this. I was using bad data and other people were using good data and they could tell the difference. So the best data is stuff that's banned and the best experts are the ones who risked it all and were unpaid. Unpaid except for their best-selling books and podcasts.

And then this was the important part because I got this because I asked people okay if you do all this how do you know the data is right? Like ultimately how do you know data is right? And the best answer I got is that somebody compared it to what they already knew. And when you compare it to what you already know you do your own assessment. So you don't just trust the data. I was told you do your own assessment of whether it looks reasonable.

Now a day ago I would have said this is confirmation bias and that it just looks like people had confirmation bias and they worked backwards to who the right data was and who the experts are. That was my old model of the world. But since this process got to the right answer I have to revise that and apologize because I was wrong.

I'm being told that there's a different doctor who's more credible somebody called Dr. Lee. Now I don't know anything about Dr. Lee but I'm sure he's not writing a book or monetizing his opinion anyway just like the other experts although they did write best-selling books and probably doing speaking engagements and are very very famous people now but they didn't have any monetary interest. Just books and podcasts and stuff which are probably paying more than their medical stuff.

So this used to be confirmation bias but now I know this is actually just a common sense kind of approach.

So then the last step is the part that I was totally wrong about. This is the part where I really screwed up. But so once you've got your good data and your good experts which you can tell because you're comparing it to your own assessment of the data and the experts. So you know that's good because it's all compatible. Then you take that because that's just your assessment on one side. That's just the vaccine. And you compare it to the unknown risk of a bioengineered weapon of mass destruction and it's obvious that this is a bigger risk.

So this is as close as I can get to trying to improve my game. And I think what happened was ultimately I got lost in the weeds. I was in the weeds. I couldn't see the forest for the trees kind of thing. But it was all kind of obvious if I just go and use the good data with the good experts and it's easy to tell who the good ones are because they're banned. And then just use my own assessment to decide what was the good data, the good experts, and then compare it to the unknown risk of a bioengineered weapon of mass destruction. And I could have gotten the right answer too during the fog of war.

And on top of that one thing I was also getting wrong was certainty. I kept being on the fence. I don't know if I got a lot of criticism for that because I was like I'm on the fence. I don't know which is the bigger risk. I don't know is it the vaccine or the virus? I don't know. So I was all over the place. And I can't apologize enough for that. You know I'm sorry that I misled you and I was totally wrong. The unvaccinated were the winners. They got the right answer from the start. And impressively they got it with heuristics which beat the heck out of my poor analytical abilities. And you have my respect for that.

All right. I don't understand this comment. My inability to be wrong. How much more can I agree with you? You won't take yes for an answer. Why can't you take yes for an answer? I'm literally telling you you got the right answer and I'm accepting your analysis. What's the problem? Seriously what's the problem?

No no no. I may have misled you. Somebody says was I certain it was a bioweapon? Now let me be clear. I didn't know what the virus was. I also didn't know what the so-called vaccination was. I didn't know what either one of them. So I was a complete unknown. I should have listened to the good experts who knew from the start what the virus was and what the vaccination was. And I should have known who the good experts were because I should have used my own assessment. And like you say there were a lot of you who were right from the start and I certainly wasn't. Would you agree with that statement? There are a lot of you who through this improved let's say sort of a model for understanding the world.

And now we want tribunals. Yeah I think the people who did not use this method probably need jail time. Would you agree? I'm wondering if I should go to jail. Do you know how many people I probably killed with my analysis? Let me think. I have to live with that for the rest of my life. Those of you who were the smart ones you have a clean conscience now.

I used to worry about all the people who used this analysis and then died of COVID because they were unvaccinated. But and that actually was one of my biggest decision-making criteria because I was under the mistaken impression that there was like some COVID pandemic that swept through the country. And recently I'm learning that nobody died. So I mean I thought I was believing the government. The government idiot. I don't know how dumb I could be. I believed the government that people were dying of COVID and the government was saying that the unvaccinated were dying at a much higher rate. And now we know that that was all made up because all data is made up except for this. If I'd known the good experts and the good data I wouldn't have made that mistake that's for sure.

All right so next time I'm go