September 2020 Blogs Sep 7, 2020 - Everybody Is Lying To You

Sep 7, 2020 - Everybody Is Lying To You This blog was written back in July, but I never finished it. At about 14,000 words and only half done, I ultimately didn't complete it because my anger subsided somewhat, and I honestly thought this lockdown bullshit would be over by now. It's better, but there's still a lot of bullshit out there, so here's my incomplete blog post. I'm about to save your life. There is a coordinated, intentional effort to craft, support, and enforce deliberate narratives in the news media. There's a lot of reasons for this. The news media has advertisers, and when the advertisers don't like what they are doing, they use their business as a way to influence them, or even get them fired (such as Disney and T-Mobile pulling out of Fox News due to Tucker Carlson's show). The majority of the advertisers for news channels are pharmaceutical companies - how often do you think you are going to see negative reports on them? I mean, you do, but usually after a smaller organization outs them to the public. They won't be the first. They'll sit on Epstein stories until someone else reports it first. They hide the news, they don't report it. But more than that, a lot of journalists - particularly the younger ones - are ideologically motivated. They see reporting as a form of activism. Activism always puts the cause first. A real journalist will put the news first. So an activist might change, ignore, or denigrate a story which they feel works against their cause. They intentionally choose bias over the objective, and in doing so, make the news something they want to report when it is in their best interest, rather than an obligation to a neutral truth. But perhaps most of all, there's only 6 corporations which own the various news organizations that are out there. From the top down, they can dictate which stories are delivered to views and how. Ever see that video of all those dozens of affiliate news stations reading the same script, word for word? And these 6 corporations are conglomerates that own other corporations. The same company (News-Corp) owns both Fox News and the Wall Street Journal - do you think one of these will ever report on the malfeasance of the other? You won't see NBC reporting on Comcast, or ABC reporting on Disney. But they might report on each other. For instance, when Disney is interested in purchasing Warner Brothers, do you think they might use ABC news to negatively report on WB in order to drive the stock prices down? Everybody is lying to you, all the time, about everything. This is the universal truth that you must accept if you do not want to be one of the mindless idiots out there, accepting the news at face value. Understandably, if you don't really give a crap about politics of some country halfway around the world, the lies may not really affect you. You aren't going to change how you vote, you probably won't actively work towards any particular goal in that area, and generally, the net effect of these lies will probably only reinforce prejudices that you are already have, so the needle is unmoved. But with the COVID-19 situation, the lies directly affect you in very devastating ways. Maybe you've just been locked up in your house for a few months. But maybe you lost your job. Maybe you couldn't attend the funeral of a loved one. Maybe you missed your cancer screening. Maybe you haven't seen your extended family, or if you have, they'll only meet you outside sitting on opposite sides of the yard with masks on. All of this is because of a very dangerous - and very much false - narrative built around COVID-19 that has deliberately been put in place to generate as much fear (and with fear, compliance) as possible. And I'm going to teach you how to spot it. Because it won't be the last big lie. Heck, it isn't even the most recent. If you do not have the knowledge and techniques to be able to see through the bullshit, you will become a victim to it. It's no longer just about being misinformed. There's a body count now. It is your duty and your privilege to see through it all. This will be a three part essay. The first will attempt to show the various ways in which the liars mislead towards their various goals. The second part will zoom in on the one part that I think it the most convincing lie they can tell - the appeal to statistics and science. And the final part is a set of guidelines that you can follow to see through the propaganda and start thinking for yourself. Because thinking for yourself is the last thing they want or need, and that is justification enough. - How To Spot a Liar I love getting in internet debates, and in each debate, my attempts to prove my side and disprove my opponent's, I've spent hundreds, if not thousands, of hours making a concerted effort into seeing through bullshit. This has created a set of unwritten rules based on experience - a sort of gut instinct - that has proven correct many, many times over the years. But a gut instinct is incredibly difficult to convey to someone else. Below are a set of... well, let's just call them "liar techniques" that I frequently see used in the news media. This not an exhaustive list, since it is hard to put into words what my experienced gut knows unspoken, but it should be a pretty good start for an amateur liar detective to being work. The Myth of Consequences Possibly the biggest way that the media liars succeed is by people thinking there are consequences for them being liars. That is, if they print something that is untrue, they could be punished, so they don't do it. Slander and libel which destroys lives and livelihoods would open them to lawsuits. That they have editors and fact checkers that would find and correct any errors before going to print. That if they were outright wrong, someone would notice and point it out. Fox News would keep CNN honest, and CNN would keep Fox News honest. Basically, the premise that there is an incentive to be actual journalists, and a disincentive to be a horrible liar. Well, I'm sorry to say, it's actually the other way around. There are disincentives to be actual journalists and liars are frequently rewarded. The news media is run by advertisers, and the more people they have watching, the more they can charge for advertising. This makes a very strong incentive to be the first with the news - even when it hasn't been fact checked or reinforced by multiple unrelated sources. It creates an incentive to sensationalize the news (tropical storms get the same fear-based reporting as category 5 hurricanes) and to focus only on stories that are unusual or exciting (man bits dog, if it bleeds it leads). But that's just a selection bias, and arguably, even the best news sources fall victim to it. The best ones know this and put policies and procedures in place to try to minimize it, but they can never completely escape it. But these days, the news media actively seeks out this bias. The people in charge of CNN or Fox News have active political aspirations and intentionally bias their news towards particular political parties - intentionally and without remorse, in the worst of yellow journalism. How can an editor or fact checker do their job if the people who reside higher than them are making decisions for them? And when it comes to consequences, there are virtually none. Lawsuits take a long time, and defamation lawsuits are difficult to pull off and tend to result in small payouts. You've ruined someone's life, made millions off it, and a decade later, you have a slim chance to be forced to give a fraction of that back. There's no incentive there. Look at the Covington Kids debacle. Last I heard, they were suing the new channels which intentionally and maliciously defamed them. It might be years before we see the outcome of that case, assuming it isn't settled out of court first. People with money are not afraid of consequences, because we've built a system of consequences around the very thing they have in abundance, money. Talking Points Talking points should be familiar to anybody at this point, but let's go over it a little bit anyway. Essentially, repetition is the key to forming a narrative. If you say something often enough, it will just get stuck in people's memories - especially the ones barely paying attention. This is an insidious and dishonest thing to do, but none can doubt its effectiveness. These talking points usually originate somewhere - some team or group which is charged of forging the narrative - coming down from on high and being forced into the colloquial speech of everybody in on it. Talking points are usually convenient sound bites of little actual value that get stuck in your brain like an ear wig, then repeated uncritically by people who never give them a second thought. Take the recent "protests" for example. Over 700 people injured. Two dozen people dead. Millions of dollars in property damage. Entire pharmacies, Auto Zones, and Targets completely cleaned out by looters. Landmarks and statues defaced or destroyed. People hitting each other with skateboards or throwing frozen water bottles at cops. It was, by any and every definition, a riot rife with violence, and yet, almost every news media channel repeated the mantra of a "peaceful protest". The reasoning is simple. They need people to see these events as peaceful protests because they are building the narrative on the legitimacy of the protesters' anger. If it instead appears that the protesters are more interested in stealing drugs from a pharmacy, the protests stop being about police racism and starts being seen as an excuse to loot. Keeping that legitimacy going is somehow not the responsibility of the protesters, who have never asked to be disassociated from the looters or to condemn their actions. Instead, it falls to the news media to keep the lie of a "peaceful protest" going to preserve it. The protesters actually support the violence, vandalism, arson, and looting - if the "peaceful protest" talking point wasn't endless repeated, over and over and over and over again, people might start wondering how stealing televisions off a train is related to George Floyd. Once they couldn't hide the fact that the protests were not, in fact, peaceful, they updated the talking point to "mostly peaceful protests". Yes, the vast majority of the people were peaceful and it was just a few bad apples out there making the rest look bad. And those bad apples were "white supremacists" (more on that in a bit). But did you ever notice how when the media is not on your side, those few bad apples become representatives of your movement? Just a few weeks ago, people were protesting the lockdowns. They were, unlike the BLM protests, completely peaceful (they even stayed behind and cleaned up after themselves when it was over!). The worst thing they did was hold some mean signs. A few of them brought guns with them (which were never used or even threatened to be used). And the news covered these "white supremacist" rallies (there they are again!) with violent and dangerous guns, and none of the reasonable protest signs were mentioned while the same few crazy ones showed up over and over again. That narrative was built out of talking points too. Just two weeks before the "mostly peaceful protests" (which were not mostly peaceful) there were "dangerous and violent protests" (which were neither dangerous or violent). When the media takes sides, they use talking points as a backbone for their narrative. They become the structure on which the reporting biases itself. Can't Defeat the Truth, Defeat Its Proponents This one is fairly obvious. I'm sure by now, you've noticed the absurdly common trope of labeling ideological enemies as the worst possible thing? If you don't want shitty video games, you are a misogynist. If you stand against social justice warriors, you are the alt-right. If you thought The Last Jedi sucked, you are an incel. If you are okay with separate bathrooms, you are transphobic. If you don't support Black Lives Matter, you are a white supremacist. If you don't think Trump was working for Putin, you are a Russian puppet. The progressive movement is largely built on the negative labeling of their foes. This is almost a form of talking points, given how repetitive it is, but its goes is to shoot the messenger before they can deliver the message. It's a form of poisoning the well. If your opponent can not be defeated, he can be slandered. When this happens, people won't listen to what he has to say. Maybe it is a tribalism thing, but who is going to take someone called a "white supremacist" seriously? The problem is, this approach has been overused so much that it quickly loses effectiveness. Calling people a misogynist doesn't work as well as it used to. It used to get people fired, or make them afraid to speak out. Now that doesn't work, because they've called too many people misogynists and it's lost all meaning. I'm actually curious what label they'll come up with after white supremacist loses its value. Further down the page, I'll give tips on how to see through the bullshit, but I'm just going to say this now. If the only argument anyone has for their position is that their opponent is bad because "label", feel free to completely ignore anything THEY say. Real people with real positions are willing to defend them against all attacks - if you are right, you shouldn't have trouble feeling like you can prove it. If you resort to name calling, whether your position is superior or not, you are believing it for the wrong reasons and can not defend it like a decent human being. Just remember, it is never a bad thing to doubt science. It is the definition of science, and unwavering faith in the accuracy of science is about the most anti-science thing you can do. Skepticism is the foundation of truth, so don't let anybody tar and feather a skeptic as being ignorant or stupid. For instance, there are many, many reasons to be skeptical of what you are told about vaccine safety and efficacy - Big Pharma is greedy and amoral (if not outright immoral), and they've proven this time and time again. They've run out of second chances on trust decades ago. Taking anything they say without a grain of salt is the true measure of ignorance. Not wanting to be associated with Jenny McCarthy is an understandable position, but not one that should deter you from the path of skepticism. Can't Defeat the Truth, Ban It This one is probably my biggest pet peeve, as I've run afoul of it many, many times. Basically, there is an insidious, coordinated effort to decide what opinions you are allowed to see and what opinions you are allowed to share. I've been banned from several forums for sharing unpopular opinions - opinions which broke no rules, but which simply stood opposed to whatever ideology the "moderators" approve of. The idea is that, because you are racist or sexist or whatever (see above), you are toxic to the community and it is their jobs as "community policy" to ban you. Nothing is farther from the truth - disagreement is the basis of discussion, and the purpose for discussion groups to exist in the first place (ever been to a forum where everybody agrees on everything and just pats each other on the back for having such righteous opinions? I'm sure you have, little else remains these days). But it isn't just forums doing this. It would be bad enough to get suspended from BoardGameGeek for saying Black Panther is a terrible movie, but it goes much, much higher than that, and affect many more people. Twitter is perhaps the king of this. They ban people for their opinions all the time, and even have a petty little punishment where bad opinions can get your blue checkmark taken away. They've made it so that your tweets can be shadow banned (other people can't see them, but you don't know this), or you can be blocked, have your tweets chosen to not show up in responses, and so on. Recently, they've taken to editorializing Trump's tweets, protecting them or adding "fact checks" as a way of undermining his public speech. Facebook is the same way. So is Google. Google has actually removed files from people's personal cloud drive (someone's personal copy of the Plandemic movie) for ideological reasons. YouTube is probably the absolute worst, as they demonetize videos they disagree with and the algorithm has made some many ideological bannings that you'd think the algorithm was voting blue in November. Reddit is basically a collection of smaller forums, but admins step in and replace moderators, protect communities they don't like, and outright ban them - ask Reddit why they booted the_donald. It wasn't because they broke any rules. It's because these companies are all very progressive, and they seek to manufacture consent by banning dissent. The worst part is, the average person doesn't even realize this. They are very insidious in how this plays out. It's very deliberate, so as to hide their own banning efforts from the average person. Things just disappear, and you didn't see it, you'd never know it was there in the first place. This leads to casual users who weren't paying attention to seeing the manufactured consent and thinking that the dissent (which they can't see) must not exist. You don't know what you can't see, so how do you know that what you can see isn't completely deliberate? It's also worth pointing out that labeling people (see previous entry) becomes the justification for this behavior. If you are pro-Trump, you are a white supremacist. A pro-Trump forum can be banned because being a white supremacist is against the terms of service. Amazing that you can ban people because of labels that you, and only you, applied to them. It's be like me walking up to a random person, calling them a pedophile, then beating the shit out of them because think of the children. Illusionary People This is, I think, one of the core principles that the progressive movement uses to underscore their moral claim to supremacy. Basically, they make a claim that some group says or believes something. Why they get to speak for this entire group, I don't know. And if anyone who is actually a member of this group should speak out with a different opinion, then they don't speak for the group (or worse, get labeled as an Uncle Tom or having internalized misogyny or something). So basically, there is a consensus, and everyone not in that consensus doesn't count. But ultimately, this group is largely imaginary in the first place. Let me give you an example. A guy recently got fired from the Flash tv show for some less than admirable tweets from years ago. In them, he made fun of domestic abuse. The stance I saw in the discussion was that it was wrong to make fun of domestic abuse survivors because it might make them relive trauma, or at the very least, is extremely disrespectful to their desire to move on. Here's the thing. No domestic abuse survivors complained about it. It was complained about on their behalf, in absence of complaint, or potentially before any real complaint. Personally, I think we need to wait for the actual complaint because only then can we judge the true extent of the offense. What if the complaint was "I think that joke was inappropriate, but I'm an adult and can handle it"? Worse yet, what if a domestic abuse survivor actually decided to come forward against the narrative and say "I didn't mind it" or "I actually thought it was funny"? Why are we deciding what we area allowed to say based on the permission of some unrelated, uninvolved guy on the behalf of what he THINKS this group of people want or need? Essentially, he has created an illusionary people. They don't exist. They are theoretical. And we must be careful not to offend the purely theoretical people, which he created based on what HE wanted to see. HE thought the jokes were offensive, so he invented a bunch of victims to justify his opinion as a fact. You see this A LOT with the "think of the children" brand of defense. Essentially, there's too much violence on television. Our children are exposed to it and might exhibit signs of violence themselves. Mind you, there's no actually evidence of this. Study after study has been done, and there's plenty of evidence that it doesn't affect them (an entire generation that grew up on Loony Tunes and don't drop anvils on each other equally proves it). At the very best, you can say that in extreme cases, it is inconclusive. Therefore, doing this to benefit "the children" is a misnomer. This is done to appease the parents, and they are using illusionary victims to morally justify their opinions. Another place you might find illusionary people is those darn white supremacists. You know those "mostly peaceful protests" that turned violent due to the actions of a few select white supremacists? Well, they don't exist. Or they don't exist in any significant number. Instead, they are a scapegoat. They are invented whole cloth to be a bad guy. The week before the protests, the Boogaloo Boys didn't exist. All we have of proof they exist is a single, out of context picture of a frog patch, and a Wikipedia page that has been edited extensively by only three progressives who's other major contributions to Wikipedia are edits to the page on "incels". Invented enemies. Enjoy your ten minutes of hate. There's one more place that you often see illusionary people: when the media say "some people are saying..." Basically, they find one or two tweets - usually in isolation and by no means representative of a movement or majority - then turn those extremist viewpoints into a majority. One guy said "I think Trump eats babies" and the news reports that "some people are suggesting that Trump eats babies". See how that works? One person suggested it, but the news created an illusionary group - not because they believe it themselves, but because they want to report on it. Maybe they hate Trump. Maybe it's sensationalized man bites dog. Regardless, these are illusionary people that are created and used largely to defend against slander and libel lawsuits. WE didn't say it. Those imaginary people did! Essentially, the illusionary people are a scapegoat. Why should video games change? Because illusionary women are being hurt. Why is inclusion necessary? Because illusionary minorities are being hurt. It's never about how I'm being hurt. It's never about how you are being hurt. It's always a group of people, who are without sin and legion - pathetic imaginary victims used to justify censorship on their behalf. Because if you get up and say "I find this offensive", people are going to tell you to grow up (and you need to), while saying "this art is harming others" makes it a challenge to argue, even when it is explicitly, measurably untrue. Disagreement Is Evil One of the primary reasons why you might invent an illusionary group is to underscore your argument with an moral authority. That is, what you believe is not an opinion. It is a fact. And believing it is RIGHT, because the illusionary people are being imaginary hurt. Anybody who is against your belief is not simply disagreeing with you, using a difference of perspective or experience, they are EVIL and want to hurt the illusionary people. The argument is no longer about disagreement. It is now a battle of good and evil. When you make a disagreement about good versus evil, there is no common ground. You can not find understanding. In a battle of good versus evil, the middle ground simply lets evil win (that's why we are seeing people arguing that not being for BLM is the same as being against it). But the worst part of it is, if you think your side is completely righteous and the other side is completely immoral, you have no incentive to listen to them. You can just ignore them. That's why they label their opponents "white supremacists". Not because they are, but because giving them an evil label gives them permission to ignore them... or potentially enact harm against them. While I have problems with all the things on this list, this is probably the one that most personally offends me. I've always believed that disagreement is how you find the truth. A debate between two viewpoints is like exercise, allowing you to build and form your viewpoint muscles. The chance of either side walking away convinced is slim to none, but it is through disagreement that we strengthen and improve our own viewpoints. And if you never have to listen to the people who disagree with you, you never get the chance to strengthen that viewpoint muscle. Instead, you end up believing the dumbest crap that can't stand up to even the slightest scrutiny. That's actually how you can spot someone who does this often. If they are willing to spout something absolutely moronic without shame, it is because they haven't done their wisdom training. Unchallenged, bad ideas fester and rots the mind. That's the only way you can come to the conclusion that your opponent is somehow evil - because you never listened long enough to know what he really stands for. Circular References This is a classic. I've seen this used repeatedly back in the GamerGate days, and I still see it used now. Basically, how it works is that you make a claim - usually about someone directly - without any evidence. Then you get someone else to quote this claim (Wikipedia is usually quick to do this, because Wikipedia is the biggest bullshit purveyor there is). Once someone quotes you, you then quote THEM, quoting you. Essentially, you give legitimacy to your original claim, not through facts or evidence, but from a circular reference. See? I'm right because CNN said so, even when CNN is saying they are right because you said so. Essentially, you become sources for each other. Any claim can be invented this way. Watch for it. Gain Legitimacy Through Appearance One of the best way to lie in the media is to use appearances to gain legitimacy for your views. One classic way is to label your view contrary to its actual purpose. You see this in politics all the time. The Clean Air Act was actually about allowing corporations to pollute. The PATRIOT Act is about the least patriotic thing they've ever done. It's all about branding. Perhaps the most effective way of branding a viewpoint is to pretend that it is the result of science. For instance, we know that people are racist because this study showed that they were more likely to convict men with black faces than white. There's a lot of problems with that particular study. It is, by no means, "science", but it feels like science, and that's enough. Nobody is actually going to go read the study, and if they did, it is written like stereo instructions so few could understand it. But you can't argue with science, because science is FACT! Well, science is anything but fact. It is actually extremely open to bias. For instance, if the tobacco industry pays for a study on the effects of cigarettes, you can be pretty sure that they are going to come to the conclusion that tobacco isn't dangerous. After all, their paychecks depend on it. This is actually extremely common in science, and this conflict of interest can be found everywhere from nutrition to pharmaceutical use to even vaccination safety. I haven't checked, but I'd be willing to bet that 100% of the studies performed at the behest of a corporate interest, ultimately comes out in their favor. It also helps that nobody bothers to replicate findings (not that many could be replicated in the first place). Perhaps the most sinister way in which "science" is abused for the legitimacy of bad ideas is the claims of so-called "experts". That's not to say that people can't be experts in their fields, but being an expert doesn't make you right. It is a type of labeling, as seen above, but used to label your support "unassailable". But what happens when you find two experts that disagree with each other? During the early days of the COVID-19 situation, there were many highly qualified experts in the field of epidemiology which vehemently disagreed with the lockdowns and the dangers of COVID-19 - but their videos disappeared off YouTube. Their personal webpages were deleted. Why did we choose one set of experts to listen to rather than listening to all of them? But more than that, we often see this "expert" label applied wrong. For instance, in Florida, we had this really nice COVID dashboard which kept track of the cases in Florida. The person who was in charge of this dashboard was ultimately fired for insubordination, and the narrative instantly became "the scientist behind the Florida dashboard fired for refusing to hide data". Well, by "scientist", they mean "geologist". It wasn't her data. She was just running the web app. It was never her place nor her right to decide what data was presented on it. She's not even qualified to understand what the data means. Somehow, being a scientist in a completely unrelated field allows you to claim expertise over another in the eyes of the public. Ultimately, what it comes down to is that you can not pre-judge people because of labels, either good or bad. A person declared an "expert" can be wrong too. In fact, I think you'll find that they tend to be wrong the most often. Neil Ferguson, the expert in charge of the COVID model which said millions would die without a lockdown was wrong. He was wrong last time too. And the time before that. If he has a career at all, it is entirely due to over exaggerating every danger by orders of magnitude. He's been so wrong by so much so often that I can't believe anybody listened to him without getting at least a second opinion. Hope People Are Too Lazy to Check One thing I've noticed often is that people are really fucking lazy. There's no kind way to put it. Most don't even read past the headline - which are often at odds with what the article actually says, and usually written by someone who wasn't even involved in writing the article. But even if they do read the articles, they never follow the links to fact check the article. If an article is reporting on a particular study, they don't click on it. If an article is referencing a particular sound bite or video clip, they never go find out what happened immediately before or immediately after that carefully selected segment. Most of the time, journalists are not scientist - and by this, I mean they have no concept of math or how it is used. They talk about things like the R value or exponential curves as if they even remotely understood. They take things uncritically because they don't understand it, don't care to understand it, and probably couldn't understand it anyway. A journalist reporting on science is like an eight year old reporting on the inheritance tax: they just copy the encyclopedia and move the words around a little. For instance, going back to the Ferguson COVID model - they repeated, uncritically, that the model reported that 2 million people would die by COVID. What they did not do is how that model was computed. What assumptions were made. How reliable the output was in the past. They took the statement as a fact, repeated it as a fact, and spent more time fear mongering the outrageous number of deaths than questioning them. Turns out, the Ferguson model was bullshit, top to bottom, and history has shown this repeatedly. A ten minute Google search would've led them to the past cases where it failed spectacularly, costing the UK literally billions of pounds and resulting in the deaths of millions of livestock. They didn't check, and I'll bet you didn't either. I did though. Similarly, you are reading monumentally stupid things like "deaths have doubled in Florida since the lockdown ended" - which is technically true, but virtually meaningless. The number of deaths is a linearly increasing number. At some point, it would reach that milestone by simple virtue of it only being able to go up. Meanwhile, the amount of testing has gone and the percentage of positive tests have gone down. The number of hospitalizations and deaths due to COVID has continued to plummet - but because they are non-zero, the total continues to climb. It's like they don't understand simple math. They see a graph with a number on two axes and think it means something. In a perfect world, someone would look at an article like that and go, wait a second. The percentage of new cases is dropping, which means that COVID is disappearing from Florida, and you are latching on to an irrelevant relationship. When you say that Florida has had a huge spike in the number of cases, you missed that the testing also went up. There's more new cases because there's a lot more testing. And someone would say, "this article is bullshit" and the news organization would go, "hey, our bad" and remove, or at least edit/update the article to reflect the improved context. They don't know science but it doesn't matter because nobody checks them. We had a lockdown due entirely to the panic that the news media invented out of thin air, largely because they didn't do their due diligence - but neither did we. Use Non-Falsifiable Statements One of my favorite thing that the news media does is use non-falsifiable statements. That is, statements which can not be proven false. Because they are liars, these are generally used to mislead in a way in which they can not be checked. I already talked about the "some people are saying..." fallacy that allows them to get away with slander without taking responsibility, but it goes deeper than that. For instance, the statement that "police brutality is a serious problem facing the black community" - is it? How do you measure what that problem is? If you find even one case of police brutality, you can argue that it is a serious problem. There's never situation in which this statement can be untrue because it requires a subjective interpretation. Thus, there is never a measure by which we can make it not true. We can not solve the problem of police brutality because we do not have a quantifiable outcome in which it is not a serious problem. (You can argue that zero police brutality is the desirable outcome, but in a job where you might have to subdue an insane person with a weapon, the amount of force that is necessary will always be open to interpretation). Another one I saw recently is saying that, back to the stupid Ferguson model, lockdowns saved millions of lives. After all, the model said millions would die without a lockdown, we had a lockdown, and millions didn't die. Can't argue with that. Literally. There's no way to check if the model was ever correct in the first place. It was, even in the most charitable interpretation, a guess. You can't say that we saved millions of lives... but you also can't say we didn't. Not without parallel worlds or something. Even the fact that Sweden didn't lock down can be argued away as, well, Sweden still did social distancing and they benefited from everyone else locking down. These are tricky things to spot because they seem like they are saying something profound, but are ultimately unquantifiable or replicatable. Something like "peaceful protests" is something you can argue against, but "mostly peaceful protests" has enough ambiguity as to be worthless. How do you define "mostly"? Keep a look out for when they say nothing at all, because this usually indicates an intentional obscuring of message, or at the very least, the result of vague circular reason. Prime the Heart and the Brain With Follow Journalism is supposed to be objective and fair in how it reports the news, but what passes for journalism today is anything but. One of the most insidious ways in which they are not objective is in the use of priming. That is, they introduce a new idea to you along with the context in which you are supposed to feel about it. For instance, take the following paragraph from Politico: They can either vote down the legislation next week and face charges of obstruction amid a national reckoning on race — or advance a bill that they say needs massive changes in order to meet the moment. The dilemma was hoisted on them Wednesday after Senate Majority Leader Mitch McConnell (R-Ky.) abruptly rejiggered the Senate schedule to bring Sen. Tim Scott’s (R-S.C.) legislation to the floor. It may not be initially obvious, but the way this bit of news is phrased subtly indicates how you should feel about certain ideas. I guess I shouldn't say subtly. It's pretty obvious. "Reckoning", "massive", "hoisted", "abruptly" and even "rejiggered" are all words which convey a viewpoint, not a fact. This article, and many others on Politico, are filled with language that is intended to judge behavior even as it is first introduced as new information. Bonus points for, later in the article, using "Some say it could be a mistake..." Some say, huh? The central idea for priming is that it is easier to fool a person than to convince them they have been fooled. Once someone has made up their mind about something, it becomes incredibly hard to convince them to reconsider. By introducing news through loaded language, you can bypass the rational part of the brain, building up an emotional response - no matter how small - to the news, making it so much harder for a competing viewpoint to take hold. It's a bit of a cheat. You get stuck in a viewpoint without even realizing it. Perhaps the absolute worst example of this is John Oliver's HBO show. He intersperses facts and opinions freely, but couches all of it in a form of mocking comedy that makes it perfectly clear where he expects you to stand, emotionally, on the issues presented. Hiding under the guise of a comedy show, it is instead the worst kind of new editorialization - it is actively cruel and derisive to those that disagree with it. That his segments are often well researched makes it a shame that he displays such open contempt for those of other viewpoints. Apply Motivations and Intent to the Dead or Missing One thing which has really been irritating me recently is when people ascribe motivations and intent to people who are dead. They can not defend their viewpoints. We can not ask them what they really meant. If we are lucky, we might have a few letters on various subjects, but even those represent opinions at a specific point in time - we don't know if they continued to keep those opinions or if they changed them with age. They are dead now, so change is impossible for them. What's the point of calling a dead person a racist? He can't hurt you now and you can't make the dead feel remorse. This is happening ALL THE TIME. You'll hear how Thomas Jefferson had slaves. I mean, he did. But I'll bet Jefferson would be the first person who would love to have that discussion with you, but he's dead. I'd like to ask Lincoln about the unconstitutional things he did during the Civil War, but he's dead too. H.P. Lovecraft was an amazing writer with an amazing mind, and if I had five minutes to have a chat with him, you can bet that what he named his cat wouldn't be a priority. But people fixate on these particular things, and unfortunately, the dead can not defend themselves. Worse yet, you can't defend the dead either. After all, ascribing a belief or intent to someone who can't explain themselves can really be defended by ascribing a different belief or intent in its place. Was Lovecraft racist? Sure. But did it define him as a person? We'll never know. We can't know. The dead tell no tales. So trying to boil an entire person's life down to one particular belief they may have held at one particular point in time is absurd, but the fact that they can't defend themselves and all we have is an incomplete history to go from means that we'll never know the truth. We can't. But that doesn't stop a bunch of people from believing they know what dark secrets were held in their hearts. If someone is alive and does something wrong, talk to them about it. Ask what they really meant or if what they said was really what you thought they said. If they did mean it, it is an opportunity to discuss a different viewpoint with them, maybe changing their opinions and helping them move towards the type of person you think they should be. But dead men tell no tales. They can't feel remorse. They can't change their mind. They can't even explain themselves. Maybe you are wrong about them - but how could you ever know? Ultimately, what it comes down to is that the goal of calling Jefferson a slave owner or Lovecraft a racist has nothing to do with Jefferson or Lovecraft. It is about invalidating their legacy. The things they accomplished, the history they made, the admiration of others they've earn - it is meaningless when they are bad people, I guess. Someone who owned a slave can not also have created the most enlightened government of modern times. The two ideas do not gel with each other. And if you can trash someone for owning slaves, you can trash their accomplishments. The goals of the people who speak ill of the dead want those accomplishments - either to claim them for themselves, or to tarnish them enough to replace them with something inferior (but which aligns with their ignorant world view). If nothing else, I want to underscore the fact that the dead can't defend themselves. You don't know shit about what was in their hearts and they aren't here to defend themselves. It is, in every sense, a strawman argument. You've invented a version of Thomas Jefferson that was exactly as evil and you need him to be to further your goals. It's a foolish thing to do, but it must work and work well, given what people have been doing to statues recently. Blame the Intent of the Unintended There are certain behaviors that I don't think can be accidental, just by definition. For instance, the concept of accidental racism. Someone makes a joke, not intending in the least to offend anybody or to make any grand claims about race, and somebody says it is racist, so it must be. Can you be accidentally racist? Maybe it is because I'm Gen X, but I grew up believing that racism was a belief. It was something you choose to do. And racists are bad because the choose to do something that is bad. But if it is accidental, it isn't a choice. That's why we make the distinction between involuntary manslaughter and first degree murder. It makes a difference. You see this stuff all the time and they are usually given monumentally stupid names like diet racism or microaggressions. This leads to the mistaken impression that noticing the differences between people is making a value judgment on them, or that making a value judgment is inherently immoral. We make judgments all the time. For instance, judging H.P. Lovecraft as a racist is a judgment. So is believing that a murderer should be in prison. Not all judgments are rational or wise, nor are they all helpful and productive. But a judgment, in and of itself, is not immoral. That good versus evil thinking is the kind of mistake children make. I think that when we go down the path of blaming people for things they never meant, or even which they have no control over, we start to approach the same mistake that racists do. We should not define a person by the things they had no control over. And offending others is something you have no control over. In much the same way that you don't get to choose your race, you also don't get to choose how people interpret your actions - if you could, no doubt everybody would choose the more generous responses over offense. The argument that you should know better feels a bit weak, because what you should know better is never explicit. There's not a place where one can learn how not to offend others over imagined slights. There's no book of rules that one can consult before one speaks, and if there were, what a fucking awful society we would be living in then. Take microaggressions. "Your English is very good". This is intended as a compliment. That it is not a compliment, that it is taken in the least charitable way possible, is not the fault of the speaker. Offense is taken, not given, and this is something that we, as a society, has not yet accepted as an absolute truth. The end result is that public figures (and even a few private ones) offend people - unintentionally - and yet the news reports it as if it were intentional. One of the places where people lie to you the most is when they interpret the intentions of others for you. The guy reporting on Trump's tweets doesn't know what is intended by them better than Trump. When Trump says he didn't intend the offense that was taken, take a moment and look at the tweet and try to interpret it in the most charitable way, not the least. Never trust someone to tell you what someone else is thinking. They don't know, they can't know, and they certainly aren't authorized to speak on their behalf. If someone says what their intent is, and a bunch of strangers who have never met this person disagree, why do we always seem to take the strangers' side? We live in a cancel culture that publicly crucifies people over accidents and misinterpretation. We can not be a functional society with mob rule. It is impossible. So we must defer intent to the only people who actually know. And more than that, we can't let this misattributed intent be used against people with different viewpoints as a way to silence them without having to listen to them. A conservative is not racist. That's not their intent. A Star Wars fan is not sexist for hating The Last Jedi. That's not their intent. A person who is pro free speech is not alt right. A person who is skeptical of vaccines is not anti-science. That's not their intent. If you spend more time asking people what their intention actually is instead of just assuming it like an asshole, you might learn something. - Lies, Damn Lies, and Statistics If there is one place where the lies are the hardest to catch, it is in the so-called "science" studies. I've been arguing on the internet for several decades, and in either defending my position or attacking another one, I've always made it a point to read every article and delve into every study. It's not easy, since many studies read like stereo instructions. But it is always worth it. And the one thing I've learned is that reporters don't know shit about science, scientists aren't very good at their jobs, and every study done on people is basically worthless. These are some bold claims, but I think the fact that we locked down for three months over a virus which is no worse than the flu shows that we have made a massive mistake when it comes to how we report, understand, and respond to something called "science". I'm going to go into further detail about how all this stuff breaks down (often and with great prejudice), but first I want to briefly touch on an almost fundamentalist faith in science. Science Isn't Religion, But We Treat It Like One Science is, ultimately, skepticism. The basic idea behind the scientific method is that we create a model about how we think things work. Then we test it. If the testing goes well for the majority of things, then we start thinking of ways it shouldn't work. And we test that. If it still works, we'll adopt the model if and only if it is useful - that is, it more accurately predicts the outcome of an experiment. Science isn't about explaining the past. The Ptolemaic Model shows that we are very good at explaining things while still being wrong. Science is about predicting things. Science tells us what reaction putting two chemicals together will have. It might explain what happened last time, but it is only useful when we can use it to base future decisions on. And you know what? These models get outdated. We figure out better models all the time. Probably the most obvious example is how our models of atoms have traveled from the plumb pudding model to an entire branch of science called quantum mechanics. We replace old models with new models all the time because science has the built in understanding that science is more wrong than right, and by moving forward, we will correct our assumptions, learn new information, and understand things better. Science is skeptical of science. That's why it works. But some people defer to science, as if it were some basic universal fact. Science is not always in progress. It is static and immutable. We know everything. Every assumption we make is correct. Our science is perfect, and thus science is indisputable. At the very least, some people like to prop science up on a pedestal and defer to it over their own judgment. They are, after all, stupid. And science is smart. And smart is always more right than stupid. Unfortunately, it is this assumption that leads us astray. Because we think that you must be an epidemiologist to question an epidemiologist, it creates a tiered system of wisdom, where only those in the priesthood are worthy of true knowledge. We've created entire branches of science that were bullshit. It's worth pointing out that people once took astrology as seriously as we take climate change (maybe even more so). These days, we're not that much smarter. You'd be surprised how little we actually know about how the human body works. We have models, but they are wrong as much as they are right. Everything we know about nutrition has changed and changed again in my lifetime, and it'll change more. Medical science is probably the most immature science, but the one we accept as the most immutable. The problem is, there are bad actors out there - the liars - who want to use people's respect for science as a weapon against them. A real obvious example is how we thought red meat caused heart disease for decades based on a fake study done at the behest of the sugar industry. Only now we are learning how seriously bad for you sugar is, and how good fat it, but only after we've spent decades taking fat out of things and adding more and more and more sugar. Heart disease is the number one killer in the word, killing literally millions of people a year - and we brought it on ourselves because we trusted science, and because we trusted the people who called their bullshit science. It is absolutely acceptable to defer judgement to experts. After all, people don't have the time or effort to become experts in everything themselves. Before COVID-19, I didn't care how infectious diseases worked. Now, after a thousand hours reading through dozens, if not hundreds of articles, studies, and interviews - I still wouldn't even remotely consider myself an expert. But I know how to spot the bullshit. If you can spot the bullshit, you don't need to be an expert. You can better know who to trust, or more importantly, who not to trust. The following points broadly fit into one of three categories - bad data, bad scientists, and bad understanding. Bad Data Creates Bad Conclusions We're going to start by looking at bad data, and how it can corrupt a study and thus corrupt the conclusions we take from the study. Nowhere has this been more obvious, and more egregious than the recent COVID-19 debacle. In the early stages of trying to figure out the threat that COVID-19 posed to the world, we simply did not have a lot of testing going on. Tests were expensive and hard to get, so we had few of them and we explicitly used them in places where we thought it would do the most good - we tested sick people to see if they were sick. Obviously, this would lead to a far, far greater number of positive cases as we only tested people we thought were most likely to be positive in the first place. This biased data created the illusion that COVID-19 was far more dangerous than it was. They tried to calculate the fatality rate of the disease using an artificially higher number. We weren't testing people with mild symptoms, or no symptoms at all, so our biased data led us to the conclusion that the virus was far more prevalent, and deadly, than it would've been if we had more accurately charted how many people were positive, but not actually in any danger. Early on, due to this bad data, we had people suggesting that the IFR (the infection fatality rate, or the chance of death when infected) to be as high as 5% or even 9%. However, after more tests became available, we started to see that a lot of people were testing positive that had no symptoms and were in no danger. This increases the number of infections without increasing the number of deaths due to infection, reducing the IFR down to... I forget where it is at now. 0.1%? It might be less than even that because of a bunch of other extreme failures in data are still not being controlled for. The end result is that we started to panic about something which wasn't really that dangerous. It is easy to blame the lack of data for this, but the truth is, nobody ever bothered to put that data into context in the first place. Nobody stood up and said that due to incomplete testing strategies, we are missing a big part of the picture - and in every way, the stuff we are missing would considerably reduce the danger of the virus. We knew this ahead of time (we had good data from stuff like the Diamond Princess cruise ship which showed early on that the majority of the cases would be asymptomatic and not dangerous), but nobody bothered to use that perspective to frame the bad data. The complete inability to recognize bad or incomplete data as bad or incomplete data is probably the greatest threat to science that we face. And it isn't just laymen doing it. Scientists do it all the time as well. In fact, they seem to be the most blind to bad data of anyone. The False Positive Fallacy There's this really weird trick to testing that not many people realize. No test is perfect. There will always be false positives (people who test positive but aren't) or false negatives (people who test negative but aren't). Better tests reduce this number, but there is always the possibility. The trick comes in the fact that in some circumstances, the false positives can outnumber the true positives, even with a low false positive rate. Part of this comes from the mistaken belief that that the false positive rate is based on the positive results. So, like, 100 people test positive on a test with a 1% false positive rate feels like only 1 of those 100 is a false positive. But that's incorrect. The false positive rate is based on the number of healthy people who will be misidentified as unhealthy - and if you test a huge number of healthy people, more of them will be erroneously declared as unhealthy. So, example time. Let's say you have a 1% false positive rate. This means that if you test 100 healthy people, 1 person will be falsely identified as unhealthy. If you test 1000 healthy people, it's 10 people. If you test 10,000, it's 100 people. If you test 1,000,000 people, it is 10,000 false positives. The number of true positives should not change, which means that if you test a significant number of healthy people and a small number of unhealthy people, the number of false positives will be higher! Stealing an example from Wikipedia to save myself some math, say you have a breathalyzer test with a false positive rate of 5% (5% of the sober drivers will be declared drunk, but 100% of the drunk drivers will be declared drunk). With a rate of 1 in a 1000 drunk drivers to sober drivers, you will have 1 true positive and 50 false positives. This means that the chance that any positive result is a true positive is about 2%. With early COVID-19 testing, someone did the math. The plugged in a bunch of numbers about infection rates into the formula to see how it affected the outcome. And they discovered that for infection rates below 40%, the chance of a positive result being a true positive was only 50%. We don't know the true infection rates of COVID-19, but recent antibody tests have suggestion it might have previously infected 15%-20% of most populations. This means that as we started testing more and more healthy people, the number of false positives almost certainly out paced the true positives some time ago. What this means is that listing all those positive COVID tests as evidence of the spread of the disease was actually extremely misleading, as many or even most of those were false positives. The effect of this is that COVID case numbers are virtually meaningless. The only stats that have any meaning are the ones we can definitively measure, such as the number of hospitalizations or deaths due to the virus. Except... Reporting Irregularities It turns out the the number of deaths due to COVID might actually be significantly lower than reported. This is due to two major things. The first is that how we report COVID deaths is extremely inconsistent, between countries, between states, between hospitals - we aren't measuring the same things in the same ways, which makes the collective data set difficult to compare. For instance, New York state added about 4,000 COVID deaths to the running total on one day. These were people who never tested positive for COVID, but instead died without testing and were suspected of having COVID. Maybe they did have COVID, but we can't build good science based on speculation. Worse still, because they added all these deaths on a single day, it created the appearance of a massive spike in deaths on the day they were reported, when they should've been more evenly spread over the previous weeks. The other reason why the death rate might be significantly lower is because the treatment of COVID might actually have been the cause of a bunch of deaths that were blamed on COVID. I'm talking, of course, about ventilators. Ventilators were made a very big deal about early on during the pandemic, and everybody seemed to think they needed them by the boatload. But ventilators are actually extremely dangerous medical devices. To go on a ventilator, you have to be put on a cocktail of paralytics, sedatives, and other pharmaceuticals, sometimes tractotomies are required, while the device forcefully expands your lungs to help you breath. If this wasn't a traumatic experience enough for your body, then you have the issue of bacteria around the mouthpiece getting into your lungs and giving you a respiratory infection. All things told, if you go on a ventilator, you have something like a 70% chance of dying before you come off. And that's separate from COVID. There's a group of doctors who have been speaking out against ventilators for years. We knew ventilators were dangerous, but it became a political thing. The governor of New York was praised so much for his ventilators that it soon became policy in New York hospitals to put even suspected COVID patients on ventilators - even when they didn't need it. Turns out that COVID-19 doesn't affect your lungs (which the ventilators force to breath) but in the way oxygen attaches to hemoglobin in the blood. A ventilator doesn't really do anything, and increasing oxygen intake through a non-intrusive breathing mask is more effective. Ultimately, this means that the places that used the least amount of ventilators actually had few deaths. We were killing healthy people or people who likely could've recovered by using the wrong tool to treat this disease. By that's not what this bullet point is about. This is about reporting irregularities. Every single person that died on a ventilator - almost certainly DUE to the ventilator - is declared as a COVID-19 death. This makes the disease look far more deadly that it actually is. It also explains why a full two thirds of COVID deaths in the US are from the New York state area. Humans Are Different, Don't Easily Group It is a commonly held notion that human beings are very different. That is, if you talk to a white man from Kentucky with a third grade education, you'll have a very different experience than talking to a black woman from Trinidad with a PhD. And these are just a few superficial traits that might lead to differences. Whether they grow up in a large city or a rural village. Whether their parents divorced. What experiences they had growing up. Everything that happens to us shapes us, but there's even biological differences that drive us. Your level of hormones can greatly affect your temperament. Even our diets can affect us. We're just different. Top to bottom. And groups of us are different too. What this means is that it is virtually impossible to make a study which can accurately predict human behavior. If you do a study with one group of people, you can not replicate it with another group of people. Every surveys, where you are essentially asking people their opinions on things, can change by a significant amount just by phrasing the question another way. About the only conclusions that you can truly draw from a human based study is that this specific group of people did this thing, but if you change the thing or change the group of people, it probably won't hold. You'll often see giant sweeping generalizations about the human populace. Stuff like "gamers are sexist". Oh? That's a pretty broad and eclectic group of people who probably are more different than alike. Why do you think they are sexist? Well, we did this study where we interviewed 30 gamers with this questionnaire... You see where I'm going with this, right? Pick a different 30 gamers, rephrase the questions, you'll have a different outcome every single time. When people try to make studies in which the arbitrarily group a bunch of disparate individuals together and make claims about a larger group of people - they are almost always bullshit. For lots of reasons, but recently, it seems like the biggest reasons is to push an agenda. Now, that's not to say that you can't group people together. It is just really hard to do, and not something you can take for granted. You must always be aware that since you can not control humans, you can not control for human behavior. Check the Sample Size One area where you can start to make assumptions about human behavior is with a statistically significant sample size. The law of large numbers basically says, as the sample size grows, the closer the results get to a true average. If you flip a coin 3 times, it is entirely possible to get 3 heads in row. But if you flip a coin a thousand times, the results from the coin flips will start to approach the theoretical 50% heads, 50% tails. This also applies to human behavior. With a large enough sample size, the trends you might see start to approach meaningful. When I was researching the efficacy of homework a few years ago, I'd come across multiple studies that would compare homework practices against very small groups - usually one classroom of kids. Can you really make broad claims about the effectiveness of homework based on a single group of children? Of course not. The same size is small enough that the differences between the children are more important than the similarities. But if you tested thousands or even millions of kids, the individual differences start to give way to the statistically significant trends (and as it turns out, when they do measure thousands of kids, the efficacy of homework virtually evaporates). Always look at the size of the same. If it makes broad claims about large groups of people based on the behavior of just a handful, that's awful and you should immediately be suspicious. I'd even advocate for throwing out the study outright, since I'm not sure you can make any meaningful declaration about the many from such a few. And it is worth pointing out that sample size alone does not completely control for bias. When they do phone surveys for how people might vote, these can change considerably based on how the questions are phrased. Sometimes, the law of large numbers measures how people respond to those specific questions rather than how they might think or act independently. Check If They Control For All Possibilities One thing that has always irritated me is when the studies do no control for variables which might have a non-zero effect on the outcome, or otherwise ignore or downplay these variables as irrelevant. For instance, I recently was sent a study which said that our justice system is racist because black men had, on average, about a 30% longer prison sentence assigned than white males. I was like, woah, that's serious. Let's check out the evidence. Well, it turns out that women - regardless of race - had 30% shorter prison sentences than white males. This means that it is better to be a black woman than a white male when it comes to sentencing. Seems like race isn't the primary factor in this discrepancy. I continued to read the study and came across a section where it stated that the sentencing differences between white and black males has been shrinking over the years, largely due to a change in laws specifically related to crack. In other words, this one difference made for a significant different in sentence lengths. And changes to those laws reduced the divide. So, it seems like a single factor (how we punish crack dealing and possession) accounted for a significant, even majority difference in the sentencing between white and black males, simply because more black males were involved in the crack trade. Not only that, but race made virtually no difference in how female inmates were sentenced. It really seemed like the study buried the lede there. It's hard to come to the conclusion that race was a defining factor there. Race was just what I call a single scoped correlation (see the next section). The actual discrepancy is due to a different variable - one they even acknowledge, but which they do not consider at much length. I was reading an article put out by the Lancet recently (which had to retract a bullshit study, because I guess they never bothered to check if the data used in the study came from a legitimate - or even existing - source). Anyway, the article is about how lockdowns are the only thing which can account for COVID-19 going away. They even bring up a bunch of variables which most definitely could affect the outcome of an infectious disease: herd immunity, climate, population density, and a few others. They bring up these variable which must have a non-zero effect on the infectious disease curve and hand wave them away as having no evidence. Excuse me? There's lots of evidence that this stuff matter. Hell, you don't have to be trained in epidemiology to realize that a sparse rural village won't see the same kind of deaths as a metropolitan city. Like, you can't just ignore variables that make a difference? So, how did they come to the conclusion that lockdowns worked? Well, that leads us to the next section. Beware Single Scoped Correlation Everybody knows of "correlation does not mean causation". If you don't, it basically means that because two things seem related, it does not mean that one caused the other. For instance, did you know that the number of people drowned by falling into a swimming pool correlates with the number of films Nicolas Cage appears in? Obviously, we can't save lives by just asking Nicholas Cage to be in less movies. One does not cause the other. One place where this insidious concept seems to take hold is in the single scoped correlation. Basically, if you look at data through the lens of only a single variable, it might look like something is causative when it is not. The example I saw most recently is "evidence" that the lockdowns worked by comparing the number of deaths in each country to how soon the countries locked down (based on how many deaths occurred before lockdown). They suggested that the countries that locked down sooner had better outcomes because... well, just look at the graph! The lines kind of look similar. Except for the fact that lockdowns are a major change in society - something so intense, so traumatic, and so broad that no part of life could be said to be completely unaffected by it. So traumatic were these changes, that you would be able to clearly see the lockdown making a difference regardless of the measures you use. If you use any other measure except for the one they used, you'll see no effect at all. Can you really say that A caused B when you should be able to see B from space with your naked eye? Why does the relation only appear when you look at it just this one way? You see this stuff a lot, actually, so it is worth looking out for. As I mentioned above, that study that compared race to prison sentence length is another example where you have one variable (race) which creates the appearance of relationship, when the actual causal variable is something else entirely. If you want to prove that something is the cause of something else, you should be able to find multiple ways in which they relate. That still won't prove anything (it could be a third variable which is causing the first two to appear linked), but you'd at least have a better case for it. Basically, if you want to make something seem related to another, you can always find some variable by which they appear to be (in the manner you wish them to be). If I wanted to show that racism is related to how many Agatha Cristie books one owns, I have no doubt that there will be some variable, some graph I can make, that will make that case for me. But just like Nicholas Cage isn't drowning children (at least his movies aren't), correlation doesn't imply causation. Always demand a relationship that you can see from numerous angles. Fuzzy Requirements Create Fuzzy Falsehoods I think I've touched on this a bit in the previous sections, but the long and short of it is, bad data creates bad conclusions. One of the ways we get bad is by using fuzzy requirements. For example, in order to calculate the IFR (infection fatality rate) or the CFR (case fatality rate), we basically take the number of deaths and divide it by the number of infections/cases. The problem is, how do we measure the deaths? It differs between different countries, states, and cities. If we had clear and universal rules for measuring deaths, even if it was not the most accurate way to count them, at least we be consistent enough to be comparing like things to each other. If you have Germany counting COVID deaths one way and Spain counting them another, then it can give the appearance of Spain having a worse outbreak simply because Spain includes deaths that Germany wouldn't. Here's a fun example from history. When the polio vaccine was released, it didn't actually work. It actually caused polio, and the number of cases went up, not down. To cover up this fact, they changed the definition of paralytic polio. Before the change, it was (I think) a case of paralysis which lasted 48 hours. After the change, it was a case of paralysis which lasted 30 days. Because a large number of paralytic polio cases would clear up within 30 days anyway, these had the net effect of making it appear like there were fewer polio cases after the vaccine was released. You might look at the fact that cases went down as evidence of the vaccine working (single scoped correlation), when in fact, you are looking at a completely different definition of paralytic polio before and after the vaccine. There's some more fun going on with polio. Polio is a virus which causes various neurological problems, and it was not something that they often tested for. If it looked like Polio, they said it was polio. However, after the vaccine came out, doctors were so convinced that polio couldn't be the culprit in vaccinated children, that they immediately ruled out polio as the cause. This means that the same symptoms would result in different diagnoses depending on whether the physician believed that vaccinations work. So there were a lot of things that would've been declared polio that suddenly started being diagnosed as other things after the vaccine - and the kicker is, they might have been these other things the entire time! They may have been over-diagnosing polio in the first place, leading to misdiagnoses, and only after ruling out the obvious answer did they start to recognize that there were a lot of different diseases causing these particular symptoms. All of these problems stem from the fact that modern medicine (and even not so modern medicine) is usually based on the subjective interpretation of the doctor. Because these determinations are so fuzzy and imprecise, it greatly affects our ability to make informed conclusions about them. We can see things in the data which aren't there, or which imply conclusions which aren't true. Calling Someone a Scientist Doesn't Confer Authority We're now going to move into the "bad scientists" portion of the essay. The next handful of sections are about how scientists screw up, or how we give scientists the benefit of the doubt when they haven't earned it. I think the first thing that someone must do to truly appreciate science is to realize that scientists fuck up all the time. Sometimes, they even do it intentionally. If you can't come to grips with this simple fact, then mindless adherence to "the science" is to just give into a form of authoritarians based on imperfect, and sometimes incredibly stupid, overlords. Probably the most obvious place this happens is just by a news article declaring someone a "scientist", like that word has any value in an of itself. I think I mentioned the "scientist" fired from doing the Florida COVID dashboard, when they were a geologist and not in any way an expert on infectious disease. My first encounter with this was maybe a decade ago. I read some news articles about how 400 or something "scientists" signed a form that said that climate change wasn't real (or at least man made, I forget). However, if you looked up these scientists, almost none of them were actual climate researchers. There were psychiatrists, sociologists, medical doctors, and all sorts of unrelated fields. They could be claimed to be scientists, and no doubt, some of them may have been very smart people - but they weren't necessarily trained in or authorized to speak on climate change. To sum it all up as "400 scientists agree" is misleading as hell. The worst part is that we still see this particular bit of publicity today. Just a few days ago, I read about 1600 scientists that said that protesting was important because racism was a greater threat than disease. I haven't looked into each one individually, but how much you want to bet those 1600 "scientists" aren't experts in infectious disease. I don't even think there are 1,600 infectious disease experts in the entire world. It's really disingenuous to say scientists in this context, because it implies they are more informed on the matter than the average lay person, which may not be the case. Ultimately, appealing to "scientists" and "following the science" is just an appeal to scientific authority. I like science. I think it does good work. But the reason science does good work is because it has a built in skepticism that allows it to overcome bad work. Even when the world thinks transitive fats cause heart disease, we can be sure that the skeptical safeguards built into the scientific method will one day prevail over intentionally bad science. Plus, giving people authority based on a job title is just kind of stupid anyway. I was a programmer and I had co-workers that could call themselves programmers, but I don't think I would qualify them as good or even competent ones. I'm sure you, whomever you are reading this, probably have less than adequate coworkers yourself. How would you feel if they used their job title to covey an air of authority over others? Biased Scientists Create Answers, Not Find Them Unfortunately, there is a significant problem with bias in science right now. It's a complicated issue, but what it essentially boils down to is, the people who control the money, control the science. At a superficial level, deciding grant money goes will decide which studies are done. If your agenda is to prove global warming, for example, and two groups are vying for your grant - one of which has released a study saying the evidence is minimal and the other suggesting that we'll all be dead by 2040 because of catastrophic temperature rises... the one which sounds the alarm is going to get the grant, and will produce more studies of that sort, while the more conservative study will not get a sequel. The scientific method works great, but we can't replace it with capitalism. But it gets more insidious than that. I mentioned before that the sugar industry funded a study specifically to say that it was fat (and not sugar) which caused heart disease due to high cholesterol, which has led to decades of us reducing fat (and increasing sugar), an obesity epidemic, and heart disease being the number one killer of human beings by a country mile. It took 50 years before we realized that we had been duped, and will probably take another 50 years to course correct. You can see a similar situation with how the tobacco industry created fake studies to promote cigarettes as healthy, and Merck faked Vioxx studies in order to get a drug which caused heart attacks to be approved for headaches. So, how does one fund a "fake" study? Won't people find out? Usually, yes, but in the past few decades there's been fewer and fewer studies done to replicate the findings in existing studies. This takes money and it requires space in journals, and redoing an experiment is much less sexy than a study saying we'll be dead by 2040. Science has a major replication crisis - not only are studies not being replicated, they can't be. Some studies rely on proprietary data that is not shared with the world. It's absurd. You can also trick people by relying on the numerous examples of lies this blog has presented. People are generally bad at science, but it turns out that scientists are people too. They have biases, are easily tricked, and terrible at subjects they are not trained in. A VERY recent example was a study published in the Lancet (a usually respectable journal) which came out and said that hydroxychloroquine was dangerous when treating COVID. Because of this, the World Health Organization stopped its hydroxychloroquine trials. The problem is, the company who provided the data for that study didn't exist. The data didn't exist either, nor could it reasonably exist. By the time someone realized and the study was retracted, an enormous amount of damage had already been done to the reputation of this drug. Retractions rarely get the publicity that the wrong studies did. There appears to be a coordinated effort to tarnish the reputation of hydroxychloroquine in the treatment of COVID-19. The leading theory is that hydroxychloroquine is a generic drug that costs about 13 cents per pill, which means that none of the pharmaceutical companies make any money on it. They'd rather you take something they have a patent for, at a thousand dollars per treatment instead. So when studies were done with hydroxychloroquine in the US, they started using some cheap tricks. For one, hydroxychloroquine doesn't work without zinc (it is basically a mechanism for delivering zinc to the body), so they did a study where hydroxychloroquine was used by itself. Also, hydroxychloroquine works best when administered early in a COVID-19 infection, so they did a study where they gave it to sick people in the late stages of illness. They also compared it against a control group given vitamin C, which itself is effective in the treatment of disease (and thus not a true control group). Now, I can't comment on hydroxychloroquine's effectiveness one way or another. Luckily, I'm not in a position to suggest other people take it. But the "studies" used to crush the reputation of the drug were all kinds of bullshit. I can't say why they were bullshit, but I can say they were designed to fail. Biased science is an extreme danger to actual progress, especially medically. Studies Don't Need to Read Like Stereo Instructions It is not uncommon to open up a study and find that it makes no sense. Different scientific disciplines tend to develop their own vernacular, and it is sometimes difficult to penetrate it as a layman. But all too often, I'll find a study which is impenetrable, but which didn't have to be. That is, I'll find a study written in a purposefully obtuse manner, when it really isn't called for. Take the following paragraph from a report I read recently: Comparing Black male offenders to White male offenders who received a non-government sponsored below range sentence, the differences in sentence length between the two groups were statistically significant in two periods, including the Post-Report period, where Black males received sentences that were 16.8 percent longer than those for White males. Differences in sentence length between Hispanic male offenders and White male offenders who received a non-government sponsored below range sentence were statistically significant only in the Gall and Post-Report periods, when the differences were 9.3 and 10.6 percent respectively. There were no statistically significant differences between the sentences imposed on Other Race male offenders and White male offenders who received a non-government sponsored below range sentence. There's no profession-specific vernacular there. That's just regular English... or should be. So why does it read like that? Well, in this case, those chose to write out a table. That is, they could have a nice spreadsheet with all the values lined up in a nice chart or something, but instead, they decided to manually write out the chart as a paragraph. That's like writing three thousand four hundred twenty-four instead of 3,424. It's more verbose. It's so verbose that it actually obfuscates the data. I'm guessing that if they just put the chart up, you might not have drawn the same conclusions as they did. Check If Conclusion Is Actually Supported By the Data In my travels through the many studies of the many fields of science, I've often come across conclusions that are not at all supported by the data. In some cases, they even outright admit this, but then hand wave this failure away like it doesn't matter. For instance, this guy (Harris Cooper) looked at a hundred different studies on homework. He took the data from these various studies, put it into a multi-variate regression analysis and formulated a normalized version of the data. And the end of all this work, the data simply wasn't there that homework made a lick of difference for kids in middle school or lower. Seeing this, he suggested a time limit for homework of 10 minutes per grade level (so an eighth grader would 80 minutes of homework as the "optimal"). But the data didn't support that conclusion, and he even admits that the recommendation was just based on something a teacher made up and told him. What's the point of reading all those studies and then recommending something completely unsupported by it? This is actually fairly common, especially in social sciences. The people who do these studies tend to go into a study already knowing the answer. They don't do a study on prison sentence length based on race to see if racism exists in the justice system, they do a study to prove that it does. And they'll grasp whatever straws they can. No matter what the data says, when you get to the end of the study, you'll always read something to the effect of "see? I was right the whole time" - even when there's no evidence that they were. Or worse, they'll admit the evidence wasn't there, but then say that the absence of evidence is not evidence of absence. In short, no matter what they do, their foregone conclusion is correct. This might be a subsection to the biased scientist bit above, but it is something to be aware of, because when journalists report on these studies, they tend to focus too heavily on these unsubstantiated conclusions. I doubt they read (much less understand) the studies they report on, so they defer all critical thinking to the study. They also rarely mention anything in the body of the report, leaving you with the impression that the conclusion is justified and supported by data. Heck, sometimes, they go by the conclusions only, completely missing the fact that the actual report says the exact opposite of what they are reporting, and they simple misread the conclusion. NEVER Trust the Models I don't think it is an exaggeration to suggest that the reason why the majority of Europe and the USA went into an extended, abusive lockdown is due primarily to a single model released by Imperial College, which suggested that several million could die from COVID. Even now, Trump says that he saved millions in the US through his lockdown, based on the predictions from this one paper. The problem is, this model is wrong. It's been wrong. It was wrong with avian flu, swine flu, foot and mouth, and basically every pandemic in modern history. For example, this model predicted the avian flu could kill up to 150 million worldwide in 2005! Holy shit! So how many actually died? 440. That's not a typo. It predicted 150,000,000 deaths, and there were 440. How wrong do you have to be, and how often, before people start taking your predictions with a grain of salt? Imperial College released the code used to calculate the 2.2 million it predicted would die from COVID-19 (technically, it was a significantly cleaned up one) and nobody in their right mind would turn in code that bad for any reason, ever. It couldn't reproduce its output (they used an average of multiple runs as their "results"), it was filled with bugs that produced different output for stupid reasons, and it was poorly written and undocumented, making it code that couldn't have been ably maintained for the 10 years they've been using and adding to it. Basically, it didn't work as a program, even if the model they used was correct. It was an embarrassment, but the damage was done. We locked down over something stupid bullshit. Climate change is also filled with bad predictions. For instance, someone said global temperatures would rise by 7 degrees before 2020 - not even close. In 2008, Al Gore said that there was a 75% chance that the ice caps would melt completely within 5 to 7 years. Nope. In 2013, a group of scientists predicted that there would be less snowfall due to climate change. Not exactly. That's not to say that climate change isn't real or isn't happening, but the most alarming predictions made have been proven to be completely wrong. So, why is this? Why can predictive models get anything right? The simple answer is, predictive models are guesses, and the assumptions made to form these guesses either prove to be wrong, too simplistic for a complicated system, or might be purposefully exaggerated due to the scientists being biased (either by where their grant money comes from, or from them being activists in the first place). Modeling something really complicated, like the spread of disease world wide, can not be accurately models by simplified systems, any more than you can predict the weather by whether it rained this same time last year. Complex systems are unpredictable precisely because there are just too many variables at play. That's not to say that models aren't worthwhile. We can't predict where a hurricane will travel in three days with any accuracy, but we can narrow it down. If you understand that predictive models are guesses, incomplete and simplified, then you can approach them with the right mindset. This is a POSSIBLE outcome. And these guesses seem to universally err on the side of exaggeration When you read a new article saying that MILLIONS could die, you also need to realize that millions almost certainly won't die. But even that doesn't explain the incredible, repeated incompetence of the Imperial model. Typically, when a model gets something wrong, that is new data that can be used to improve the model for the next time. We can narrow down where a hurricane will travel because every new hurricane is a chance to test the predictive model, and improve it. But it doesn't seem like the Imperial model was improved at all after the last dozen times it failed spectacularly. At this point, you'll get a more realistic outcome if you divide their prediction by 10. 2.2 million dead becomes 220,000 dead, which is a bit more realistic. Hopefully, they'll improve their model for next time, because I'm not locking down again. Look For Cherry Picking Regression Studies Are Bullshit People Can't Conceptualize Very Large Numbers People Can't Conceptualize Chance As a player of many miniature and board games which involve a lot of dice, I've noticed that other players have a completely unrealistic understanding of the concept of chance. To give a simple example, if something has a 40% chance of happening and you increase it to a 45% chance of happening, the effect to you, as the player, is essentially the same. Basically, the law of really large numbers says that as you play hundreds, or even thousands of games, that 5% average chance will show up - but in a single game, where you roll the dice once, the difference is immaterial. Similar to how you can flip a coin and get heads up three times in a row, that 5% chance will likely not be evenly distributed over just a few games. I think this is important because with COVID-19, you'll get stuff that says that IFR is 0.1% or something, which means that 1 out of every 1000 people infected with it will die. But that 1 person is almost certainly not chosen at random. It is certain that some people will be more susceptible than others (for example, someone who is 85 years old, bedridden, and has multiple comorbidities). As such, if you have a bunch of those people in your sample, you might actually end up with much more than 1 person per that 1000. The end result is that people might think that any 1000 peoples, any single one of them is at risk of being that 1 death - and this isn't true at all. That 1 in a 1000 number is, again the law of really big numbers, something which averages out over hundreds of thousands, or even millions. It is not distributed evenly, nor is every person equally at risk. With old folks homes making up something like 45% of the deaths in New York City, which itself represents 30% of all the deaths in the US, these percentages hide the actual risk for a collective risk that doesn't make any sense. You might see people drawing attention to the fact that, under the age of 18, the chance of dying from COVID is essentially 0%, while being over the age of 80 puts it at about 10%. Since age does appear to be a major factor in how deadly the disease is to an individual, looking at data that is more tailored to the individual's experience is going to produce a more realistic prediction than a general purpose one, averaged out over every single human being that exists. Percentages Hide Concrete Data Totals/Averages Only Work For Homogeneous Data - How To Beat a Liar Disagreements Are About Perspective, Not Morality Journalists Are Idiots, Read the Actual Studies Skepticism Is Science, Faith Is Religion Don't Dismiss Ideas Before Hearing Them Being Proven Wrong Is a Gift Worth Receiving Check Falsifiable Claims, Ignore Unfalsifiable Ones Identify When Emotive Language Is Being Used Labels Aren't Arguments, They Are Ad Hominem Attacks Science Doesn't Just Explain, It Predicts Extreme Claims Require Extreme Evidence Look For Quantifiable Claims, Demand Measurable Solutions Trust Nothing, Doubt Everything, Verify What You Can