 And that's what we're gonna talk about. Okay, so we're gonna do that. I don't want to spend a huge amount of time on it because, but since it's such a watched video and since so many of you sent it to me, I feel like obligated. I sent a lot of people like emails and texts and saying, I don't think it's any good, but I didn't explain. So now we'll explain. And we'll start with the good stuff because we're starting at two minutes and two, 20 seconds in. We said some things that are true, which I think are interesting and right. So we're gonna do that and we're gonna talk about that. Then we're gonna go to the stats and see where I think he completely messes up. All right, here we go. So let's see. Let's make sure you guys, I think you guys will hear this. If you can't hear this, let me know. Focusing on COVID. And so one of the things I'd like to talk about is when I talk to ER physicians around the country, what's happening? Well, because COVID has become the focus, people with heart disease, people with cancer, hypertension, and various things that are critical are choosing not to come in based on fear. And I think this is true. And I think very dangerous. And I think one of the real costs of the lockdown and of the fear mongering. Now it's true that in New York, and we'll talk more about New York, it was probably good that they didn't come in because New York hospitals, at least some of them were so overrun that it was good that they stayed away. But in most hospitals in the United States, particularly where this doctor is, and Dr. Erickson, I should have introduced him, is a physician in a network of hospitals, private hospitals, clinics in California. In California, where the hospitals are not overloaded, indeed the hospitals are empty, where the hospitals where doctors have been furloughed, where nurses have been furloughed, this is a real problem. And a problem that should have been addressed early on. Early on. So what that's doing is causing the health system to focus on COVID and not focus on a myriad of other things that are critical. Because we don't have the staff there and the major component is fear. People are saying, I don't wanna go get seen by my doctor. What if I get the COVID? So there is a lot of secondary effects to COVID. I think all of that is true. All of that is what we see. Again, particularly in hospital systems that are not overloaded, where there's no problem, which is 90% of the country right now. But yes, I mean, hospitals should be functioning normally. They should be uppatient. They should be elective surgeries. They should all be, these things should be going on until you get this massive spike, like we got in New York, if you ever get that, where you then have to stop those other processes. But that's not what's happening. And this is medicine driven by fear. Policy driven by fear. And policy and medicine dictated by politicians, not by physicians. Talked about. And so we'd like to kind of look at how we responded as a nation and why we responded. Our first initial response two months ago was a little bit of fear. We decided to shut down travel to and from China. These are good ideas when you don't have any facts. We decided to keep people at home and isolate them. Even though everything we've studied about quarantine, typically you quarantine the sick. This is interesting, right? Typically you quarantine the sick and that's absolutely right. Quarantine as in forced isolation is usually applied to the sick. And that's what North South Korea did. That's what Taiwan did. You test and you quarantine the sick. Again, the fundamental failure of the United States response to coronavirus was not testing on scale early on and not quarantine and tracking and doing the things necessary. And I'll talk a little bit more about that later on when I wanna recommend an article to you about the whole corona thing. When someone has measles, you quarantine them. We've never seen where we quarantine the healthy, where you take those without disease and without symptoms and lock them in your home. So some of these things from what we've studied from immunology and microbiology aren't really meshing with what we know as people of scientific minds that read this stuff every day. So that's kind of how we started. We don't know what's going on. We see this new virus. How should we respond? So we did that initially. And over the last couple of months we've gained a lot of data. Typically in Kern County, for instance, now from this point on, for the next, what? For the next good 10 minutes of talking over that it's all about the data. And this is where it goes completely wrong. Tested, 5,213 people and we have 340 positive COVID cases. Well, that's 6.5% of the population, which would indicate- It's not 6.5% of the population and this is the key. They tested 5,213 people. They had 340 something positive. Now, is that sample a random sample? No, that is people who came to the clinic because they had symptoms. They felt sick. They are by a sample. And of those that were tested who came to the clinic with symptoms, 6.5% tested positive. That's not a population. That's a biased sample. A sample that you would think was preconditioned to have a high rate of positives because they were feeling something. They weren't feeling well. Indeed, at least until recently, the CDC said don't test people without symptoms. We don't have enough tests. And California explicitly said don't test people without symptoms. We don't have enough tests. So the people that are being tested are people who have symptoms and therefore you cannot talk about the population of people who have symptoms and who go to get tested, which is another condition, 6.5% of them in his sample tested positive. It's a widespread viral infection similar to flu. Flu has far greater than 6.5%. So he's already injecting this flu stuff where there's no basis to do that. There's no statistical based on his numbers, basis to do it yet. We think it's kind of ubiquitous throughout California. We're gonna go over those numbers a little bit to kind of help you see how widespread COVID is and see how we should be responding to it based on its prevalence throughout society or the existence of the cases that we already know about. But they don't know how many cases are throughout society. All they know is the cases among those who are tested and you'll see again and again and again how this one fallacy, one fallacy and it's a fallacy in statistics. You cannot take a non-random sample and then extrapolate to society. I mean, this is like statistics 101 and it's very difficult to construct a random sample that is indeed reflective of society to make those extrapolations no statistician. I mean, a statistician could construct such an experiment and even when they do, it's very, very, very difficult. But this guy, this doctor just throws out the numbers and then well, this is the percentage in the population. Watch what he does with California. So if you look at California, these numbers are from yesterday, we have 33,865 COVID cases out of a total of 280,900 total tested. That's 12% of Californians were positive for COVID. Now note, every one of the 280,900 who went to get tested probably likely had a pre-existing condition. That had symptoms, not a pre-existing condition. They were a bias sample. They want a random sample where we tested randomly in the population, 280,000 people and 33,000 tested positive. No, this is a bias sample. So what he's doing now, he's gonna say, watch what he does with this. So we don't, the initial, as you guys know, the initial models were woefully inaccurate. They predicted millions of cases of death. Not of prevalence or incidents, but death. That is not materializing. What is materializing in the state of California is 12% positives. Well, if we have 39.5 million people, if we just take a basic calculation and extrapolate that out, that equates to about 4.7 million cases throughout the state of California. You can't do that. You cannot do that. He just said of all of Californians, 12% of all of Californians have it. Now, it might turn out that they do, not based on this. Not based on this, sorry. And it's sad because again, I think the motivation to argue against lockdowns and everything is a good motivation, but this is amateur hour. And he later says things that I would never go to this guy as a doctor. I wouldn't. Based on just his attitude and based on how he's, if he can't do this kind of, if he can't extrapolate here, I don't want to even think about what kind of treatment he provides me. I want to skip ahead a little bit to New York because the fact is, and however you look at the numbers in California, they're low, they're very, very low. And I'll give you a few more seconds of this and then we'll skip to New York. California, there's no justification in a disposed place like California to have these kind of lockdowns. It is literally insane in my view to lock people down all over the California where the prevalence of this is so low, even if you don't buy his extrapolation. But that is not true in New York. Which means this thing is widespread. That's the good news. We've seen 1,227 deaths in the state of California with a possible incidence or prevalence of 4.7 million. So he divides the number of deaths by 4.7 million, but he has no basis for the 4.7 million. He just pulled out of air. This is not true. This is just, I don't know if he's doing it on purpose or not, but this is so amateur that it is shocking, truly shocking. And the fact that five million people view this and the fact that it gets so many positive ratings and that people don't get this is a little scary to me. 0.03 chance of dying from COVID-19 in the state of California. That is called disinformation, misinformation. Misinformation. 0.03 chance of dying from COVID-19 in the state of California. Is that, does that necessitate sheltering in place? Of course not, but neither does double that, triple that. Does that necessitate shutting down medical systems? That's not the point. Does that necessitate people being out of work? So that's California. And that's, I also wanted to mention that 96% of people in California who get COVID recover. What does that mean, 96% recover? If that were the case, 4% don't recover, 4% die. So he's, even the way he's using language is just not being accurate. I think he says 96% recover without hospitalization, but 96% recover. So 4% die, that can't be right. He just said 0.03. So it's so sloppy. No significant, no significant continuing medical problems. So that's, those are important statistics for the state of California. Two months ago, we didn't know this. So is it true that over 4% of the people who get COVID have significant ongoing problems? Cause I'd like to know that, that is a huge, that's a big number. If you have a probability of 4% if you get COVID to have significant problems into the future, that's not the flu, the flu you get and you're over. But if this actually, that's what he's implying. It's not what he means to imply, but that's what he's implying. Again, you've got to listen to what people actually say and think about what it actually means. Right now, because we've, we're sharing our own data. This isn't data filtered. But it's not his own data. The data he gave us was California data on 12%, that's, that is filtered by the state of California. His own data was 5,213 versus 340 positives, which is only 6.5. So he used the number that is double his own data. And yet he keeps going back and saying, this is our own data. We're, we don't have a dog in this fight. We're not politicians. This is real data. Bullshit. Through someone. This is our own data. We found 6.5% and then California has found 12%. So the more you test, the more positives you get. The, no, the more you test, it's not true. The more, the greater the number of positives you get, but the actual percentage actually goes down. Actually goes down the more you test. You know, we'll get to in California, in New York in a minute, we'll get to that. I'll give you the numbers there. But he's just, none of this is right. Prevalence number goes up and the death rate stays the same. So it gets smaller and smaller and smaller. That is true. And as we move through this data, what I want you to see is millions of cases, small amount of death. Millions of cases, small amount of death. And you will see that in every state. And if we, and since we were talking about following the science, we're going to follow the statistics and follow the science. So I want to look at New York state. Let's do New York. They've been in the news a lot, right? And their numbers are critical. Let's go over their numbers. Cases of COVID as of yesterday, 256,272 cases in New York state, not New York City, New York, the entire state. They did a total of 649,325 tests. That's 39% of New Yorkers. See, yeah, see, here's an example. That is 39% testing positive, right? Now a few days have passed since he did this video and the number of test has increased in New York. And the rate of testing positive, even though the number of tests has grown, the rate of testing positive is actually shrunk. It's not 35%. And my guess is the more you test, the rate of testing positive is shrink because you're expanding the universe probably to beyond people with symptoms. So what he says earlier, where the more you test, the greater the percentage, it's just not true. Tested positive for COVID-19. That's their ratios. This is published data online. You can all look it up. 39% of people were tested. 39% of people were tested. Now this guy is trying to correct him. He's trying to give him an out. And Dr. Erickson doesn't take that out because he's convinced he's right. Listen. Right. There's 20 million people there who would be close to, you know, four million. Which is likely, they likely have 7.5 million cases in New York. No, it's not. Yeah. 35% of every New Yorker, it's every New Yorker who's been tested. Right. So we extrapolate data. Yeah, yeah, yeah. We extrapolate data. We test people. And then we extrapolate for the entire community based on the numbers. Oh my God. I mean, this guy would fail statistics class. Or just a, what do you call it? A reasoning class. A critical reasoning class. He would fail. That is just not true. You can't do that extrapolation. You cannot do it. And all of us need to become much, much better critical thinkers. If we're gonna stand up to these politicians and these experts and these idiots who are presenting us with data, who are convinced they can run our lives, we better be much better thinkers if we're gonna challenge this. And this is not good thinking. The initial models were so... This does not include antibody tests. Anybody tests are completely different. And there's a whole problem with antibody tests, which is false positives are very high in antibody tests. But this is not including antibody tests. This is, and you can look the numbers up because you can look it up on the websites that this is just positive negative. Right now, do you have it? Not anybody. You're right. But in those additional models, a lot of them are based off if we didn't know social distancing. Is that correct? So is it really a fair to say, obviously they're not as bad as they were because those were based on alternative... I'm just gonna skip forward a little bit. And they have a 92% recovery rate. If you are indeed... Again, what is 92% of what? Let me just find the right spot here. 1.5 million cases? We don't know. We will never test the entire state. So we extrapolate out. We use the data we have because it's the most accurate we have versus a predictive model that have been nowhere in the ballpark of accurate. So how many deaths do they have? 19,410 out of 19 million people, which is a 0.1% chance of dying from COVID in the state of New York. By the way, that is just not true. Okay. You divide 20,000, which is 19,000 something, which is what he said, divided by 7.5 million, you get 0.267. I mean 0.26, something like that, right? He just said that you get 0.1. No, I think he said 0.13. No, it's exact double that. It's 0.267. Now you shouldn't be divided by 7.5 because 7.5 is out of nowhere, but you can't even do the math. That's a calculator. I just pulled out a calculation. Now that doesn't look right. Let me divide 20 by 7.5, 20,000 by 7.5 million, and it comes out as 0.26%, now 0.2% is still low, but 0.2% is, according to Dr. Erickson, the mortality from the flu is 0.13. 0.26 is double mortality of the flu. Now that's not hysteria, but it's double. It's not the flu. It's double the flu. Now, the number of New Yorkers who have this is not 7.5. The estimates that have been made based on antibody tests are that about between three to four million New Yorkers, and even that, we don't know exactly, right? But let's say three to four million New Yorkers, that's what the latest tests have shown, have it. And if that's the case and 20,000 have died, then 20,000 divided by three to four million is something around 0.5.6%. Now it's six times more than the flu. Now, that's real. Six times more than the flu is real. Paul asked why you can't do the extrapolation. I said it, I guess you came in late, because you can extrapolate from a bias sample to a general population. Again, stat 101. To extrapolate, the sample has to be a random sample, or a sample that represents the population. But here we know the sample's not random. We know it doesn't represent the population, because the only people that are being tested are people who have symptoms. And you expect that among people who have symptoms, the percentage of people who test positive is gonna be significantly higher than among people who don't have symptoms. It's a bias sample. It's bias because of the symptoms. You can't extrapolate from a bias sample. It's very, I'll say this one more time, for those of you who came in late, it's very, very difficult to construct a sample from which you can extrapolate to a general population. It's a science to do that. And it's the seriological tests, the antibody tests, that's what they're trying to do. And it's very difficult to, many of them are failing. What they're trying to do is find a sample out there of a few hundred people that they can test. And from that test, because the sample represents different ages and different, let's say socioeconomic people and different people in different geographies and different densities and different health statuses, maybe, then you find several hundreds of those and you test them and you control statistically, then you can extrapolate to a population. But that takes huge amounts of work to do that. You can't just assume that everybody you test because they come to you to be tested. Particularly if the condition for testing is that you have a symptom, you can't assume that that is a sample. It's not, it's not representative and you cannot extrapolate. And again, this is not, this is not science fiction. This is at best, at most, one of two things. Stat 101, a critical thinking 101. Now let me see, what more do I wanna talk about here? I wanna move it a little forward to when he talks about the flu because I think there's some interesting stuff when he talks about the flu. Let's see if I can find it. Flu. And we had, we had a similar death rate. And the deaths in the United States were 43,545, similar to the flu of 2017, 2018. We had, we always have between. By the way, we're really early in this coronavirus thing. There's over 50,000 dead and we're early. I mean, we'll be on maybe the peak but there might be other peaks. And some parts of the country have experienced it. Some parts have not yet. And it's contagious, it's probably gonna spread. So we're only at 50, with 50 something thousand dead. Why are we assuming it's over? Why is that the number being used? As if it's done. I mean, I hope it's done. That would be great. Nobody die tomorrow. But this is likely to be over 60,000. That's an easy projection for me to make right now. Probably over 60,000 in the next week or so. It's likely to reach 80,000, which is what some doctors have been saying all along. And it could be much more than that if it comes back in the fall or if it doesn't go away in the summer. So why are we ready to take the numbers as they are right now and come to conclusions about this? Right? The only conclusions we come is other people sick right now, this is the number who are dying. But we don't know how many people are sick right now because we haven't run enough tests and we haven't done the kind of random samples that you would have to do in order to figure this out. It's, this is massive misinformation. Massive misinformation. You know, again, a cause I'm not unsympathetic to which is opening up the economy, opening up our lives. But this discredits the opening up our lives attempt because if you do it with bad information and bad thinking and bad extrapolation, then you're discrediting the cause. The cause is a cause of freedom. The cause is the cause of having a rational attitude towards this disease. But if you're trying to do that with irrationality, I mean, it should be obvious that can't work. Five million people have watched this video. My rebuttal to this is gonna get to a couple of thousand maybe it's tragic, tragic that this is gonna be what represents. All right, let me just do this flu again a little bit. Again, the number in New York, he did the math wrong. The number of New York is more than double the flu. And that's assuming is seven and a half million people have the virus in New York, which is a number he's plucked out of thin air. I mean, he's extrapolated out of thin air, right? But there's something else he says. Seven and 60,000 deaths in the United States. Every single year, no pandemic talk, no shelter in place, no shutting down of businesses, no sending doctor's home. That's from the flu, by the way, just to clarify. Yeah. 37,000 and 60,000 flu deaths. 37,000 and 60,000. Every year per the CDC. 30. Flu. Due to flu. In the United States. It's even as low as 20,000 some year. In 2017, 2018, it was 45 to 50,000, depending on who you read. And we don't necessarily report all of our flu tests. We do thousands of flu tests every year. We don't report everyone because the flu is ubiquitous. And to that note, we have a flu vaccine. How many people even get the flu vaccine? The flu is dangerous. It kills people. So just because you have a vaccine, does it mean it's gonna be everywhere? And it doesn't mean everyone's gonna take it because we see every year that we have a vaccine. And I would say probably 50% of the public doesn't even want it. Do you think it would be relevant if you're talking about a flu vaccine to talk about the fact that the flu vaccine is not necessarily vaccinating you against the particular strain of flu that occurs that year? That flu every year is a different strain. And when they put out the vaccine, they're in a sense guessing or estimating or making their best assumptions about which strain will hit the population at that point in time. So that even if you get the vaccine, you could get the flu because you might be vaccinated against the wrong flu, the wrong virus. That is crucial information because a corona vaccine will be a vaccine targeted at the one virus. And therefore will be far more effective than a flu vaccine. And even if not everybody gets it, the vaccine, there will be far fewer. I mean, again, it's this kind of muddled thinking that would turn me off of this doctor completely, completely, right? All right, so I don't wanna keep going. I mean, he keeps going on this stuff. And again, some of the stuff he says later on is fine, but the stuff he says about Sweden, the stuff he says about Norway, none of it's right. None of it's right. He makes basic errors. He leaves information, critical information out of the equation. And it's just a discredit, a discredit to what you're trying to do. Now, all right, so that's it. That's my thing on Dr. Erickson. What we need today, what I call the new intellectual would be any man or woman who is willing to think. Meaning any man or woman who knows that man's life must be guided by reason, by the intellect, not by feelings, wishes, wins or mystic revelations. Any man or woman who values his life and who does not want to give in to today's cult of despair, cynicism and impotence and does not intend to give up the world to the dark ages and to the role of the collectivist brought. 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