Bayes Theorem: Key to the Universe, Richard Carrier Skepticon 4

all right without further ado Richard carrier opera a muggle tech okay the one of the advantages of speaking to a large audience of young atheists is it have no end of tech geniuses to help you out of something goes wrong but a special shout-out to Darrel ray whose computer I'm relying on today all right I'm going to tell you about Bayes theorem what on earth is that I had a gentleman came up to me a little earlier and said you know I looked it up on the web and he said are you sure and I'll show you what scared him this is Bayes theorem so you're probably going what the by the end of this talk eighty three point four percent of you will understand what this means now it can be written in other ways these are those shorter forms of it and a variety of different ways you can write it but those all just conceal the fact that it's really this so that's what we're going to talk about today now why talk about Bayes theorem now I use it as the logic of explanation there's a lot of different ways that you can use Bayes theorem because it's kind of a universal theorem for any kind of analysis of probabilities but I use it in this particular way and so the rest of my talk is going to be about this particular kind of application of Bayes theorem and I'll be using it to model how we determine the most likely explanation of a body of evidence so which means the most likely cause of that evidence and for us you know we talk about is it God is it aliens is it psychic powers or is it just normal and the weight base theorem works is once you've decided on what the undisputed facts are the facts everybody can agree on you can then determine what caused those facts and that's the particular advantage of Bayes now if you want to learn more about Bates because I can't really give you a complete survey of it here I have a useful page Richard carrier dot info / Bayes calculator HTML that will tell you more cool stuff and we're all sort of I have someone who's designing an app for me it'll go up there it'll have all kinds of different kinds of calculators where you can just plug in numbers and we'll do the math for you okay so where does this thing come from Bayes theorem it's actually quite old it was originally formulated by Thomas Bayes in the middle of the 18th century but he didn't formalize it in the modern notation and modern concepts that we have now the formal proof of it and the formalization that we are familiar with now came from the greatest mathematician of the 19th century pierre-simon Laplace and then you might know him as one of the famous atheists who replied to Napoleon how come there's no God in your equations and he says I have no use for that hypothesis and then in the 20th century one of the most important contributors to the development of Bayes theorem is ET Jaynes because he showed how all scientific methods could really be reduced to it in one way or another so it's been around for quite a long time three hundred years almost [Applause] so what is it it's a description of valid inductive reasoning it's a formally valid deductive argument deductive argument with just three premises so if if you if the premises can't be denied then the conclusion can't be denied either it's it's a logically necessary conclusion given those premises and its conclusion is the probability that a claim is true and all its premises are statements of probability so why the big ass equation the reason is is that all empirical inductive reasoning is probabilistic there's always some chance of being wrong and those chances vary and since probability is by definition mathematical I mean some things are more probable than others some things are less probable than others it follows the logic of correct reasoning has to be mathematical it's the only way you can model how this kind of reasoning works and when you work it out you do the math you figure out what the correct model is Bayes theorem is what you get now this part of it here is a bold statement there are mathematicians who will argue with me on this philosophers who will argue with me on this but I'm going to prove it in a book coming soon which I'll talk about in a moment and this is my bold statement here is that Bayes theorem is the mathematical model for all correct reasoning about empirical claims every time you reason correctly you were following Bayes theorem even if you don't know it and if you aren't following it you aren't reasoning correctly and understanding Bayes theorem is therefore the key to understanding correct reasoning so you think s is pretty important for this community and this makes it a powerful tool you can use to master the universe so you can do this with it and actually that joke is kind of not funny because it's actually true the united states government has been crushing our enemies with bayes theorem ever since world war ii it's actually been so fit was so fundamental to winning the war in winning the world war ii and also winning the Cold War and fighting the Russians and so forth that for the first half of the 20th century almost all the important discoveries and applications regarding Bayes theorem or classified secret and therefore kept from the public the reason being is they didn't want our enemies to figure out that base theorem was useful now if you want to hear that story there's a really good book now in the history of Bayes theorem starting all the way back with Thomas Bayes back when the vampires were born and her name's Sharon birch McGrane and she wrote this really good book called the theory that would not die and this is the subtitle it tells it all how Bayes rule cracked the Enigma code hunted down Russian submarines and emerged triumphant from two centuries of controversy and so it's a really good book on the history of Bayes theorem and you can see how it actually has been very important it's now foundational in the insurance industry pretty much anybody who seriously has actual risk serious risk involved is going to be using Bayes theorem it's the best way to work with probabilities and situations like that there we go now before I get back to base there am I want to pause for a moment about just math in general it's important that you learn more math that you get more comfortable with math even if you're scared of math and not just so you can understand bass theorem because really the kinds of applications that you like an average skeptic might apply a base there and you don't need the really fancy hardcore technical stuff that scientists use their versions of base them you can use really simple arithmetic you can use base theorem in a really simple way so you don't need really advanced math you just need some basic mathematical principles some general ideas of probability theory and so on which you can learn that are actually kind of cool but there's another reason you need to know math and need to have a good idea of math is the fact that everybody with a special interest or a power-play against you is using it to manipulate you corporations political interest groups marketing companies they're all using math to control you and manipulate you and they're depending upon your math illiteracy in that and there's usually there's hardly any like political policy debate like a specific policy decision that can't be confirmed or refuted in about 10 second calculation if you just have some basic data what you could get for five minutes on Google so it's important to have a general idea of this and if you want some ideas like some examples there's these two really good books in numeracy mathematical and literacy and its consequences it's a short interesting book but more elaborate with more hardcore modern examples as proof enos the dark arts of mathematical deception i recommend both of these books and i think every responsible citizen needs to know how to use math to evaluate claims and arguments even your own so read these books i also want to introduce you to this lady this beautiful girl here this is Danica McKellar known by many roles as actress goddess mathematician she's actually she actually has a degree in mathematics she has a mathematical theorem named after her that she Co discovered and published in a mathematics journal and she's also now sort of become an ambassador for mathematics to the public community she's really been pushing trying to get more girls in high school interested in math and not being pushed out of math and by you know that certain social attitudes that make them feel like they have to dumb themselves down to be pretty for example or other different cultural differences so she's written a series of books starting with math doesn't suck that cover grades six through ten that are kind of like textbooks basically teaching you the math you need to know too sell at these math things and they're aimed at girls in a variety of ways it's kind of feminist in a number of sort of indirect ways but I've read these books and they're really good and I think they're just as good for boys especially if you have teenage boys that are growing up give them this book not only will they learn math because it's really good it teaches math really well in a sort of intuitive smart way but it also teaches them about girls and they'll thank you for it trust me and they're also not too dumb for adults I mean that this in fact this is the first one this is sixth grade math this book covers math doesn't suck and it's my favorite one I actually I love this book and I've learned things from it it was very very useful in fact I still reference it back when I need to do certain things now she's done other books for the other grades kiss my math and hot X algebra exposed but math doesn't suck is the book that you most will need because this book contains all the math that you actually are going to be using a lot in your daily life so there's hardly anything in here that isn't useful in some application in the normal way that you might engage yourself as skeptics but even as ordinary people and I say that that's with regard to American public high school standards math there's other math that you need to know as well that's not on the American standards and therefore she didn't included this book but this is a good book to start with for a lot of the really basic stuff now these are some other books if you if you really are scared of math or want to learn more about math but you want something that's not written for mathematicians and engineers and science students the mathematical palette is specifically written for art students and it actually teaches you a broad range of different mathematical concepts and fields it's a really good book for that purpose and then 101 things everyone should know about math is written for the general public so these are two really good books for getting you up to speed now I want to stop for a moment to talk about to go back to this book here I was once giving a tutorial on base theorem to a bunch of college professors and there was an Ivy League professor in the group and as I was going through you know explaining some of the basic ideas of the multiplication and stuff and he he raised his hand said well how do you multiply percentages because like 100 percent times 80 percent is eight hundred percent and what kind of dolt is that and I stopped and I said holy yeah so uh oh there it is it's page 163 so get you up to speed on that in case any of you are shrinking in your seats going oh I don't know the answer to that question either it's really simple you just convert percentages into decimals and multiply so eighty percent times eighty percent is 64 percent 80 percent times 100 percent is still just eighty percent and so on so little things like that you can learn from her book and that's why if you're not even at that up to speed on math you can get up to speed and it's a good book for that kind of thing now another thing I want to talk to you about is dependent probability [Music] [Applause] dependent or conditional probabilities there there's a number of different ways you can calculate these and oftentimes when you go to like Wikipedia it'll be really confusing or you won't see the a practical applications of it but there are different ways to understand dependent conditional probabilities this is one of them if you were given a test or at like a quiz and you said there's someone standing behind that door but you have to guess what sex they were it's either a boy or a girl you don't know if you don't know any information about it it's 50/50 right roughly the percentage is close enough to 5050 however but they tell you that the person's name back there is named Jane well now now the odds change you now given the fact that someone is named Jane now if we assume that 99 out of every hundred people selected at random whose name Jane is a girl then the probability that they're going to be a girl is now 99 percent not fifty percent however 99% means that there's a 1% chance that they are a boy so that's an important thing once you get information you're talking about a probability that's dependent among other another assumption being true the probability changes and base theorem is actually based on this principle and it's all about ratios the way you would figure this out is just like this the prior probability that Jane is a girl that someone named Jane is a girl equals the number of Jane's that are girls divided by the number of Jane's that are girls plus the number of genes that are boys and that's how you see the math equation really simple or at the particular on the bottom now the thing to notice here is that these are the same number so that when you see a ratio like this that's the same number and it's key because the other thing that's true about this is that the bottom is the sum of all possibilities so you have all you have the 99 girls named Jane and have the one boy named Jane that's all the Jane's there are so you put all the genes there are on the bottom and then you put the one on top that you're trying to calculate the ratio of now this internet with the significance of this is that Bayes theorem is just like that notice that these expressions the one that I've just highlighted here are exactly the same so what you're looking at is a ratio so it really works down kind of like this the probability of a claim being true equals a divided by a plus B and then you just had to figure out what the what the a is and a and the B aren't right and it's kind of this is like that ultimate ultimate reduced concept of Bayes theorem is that the probability that a claim is true equals the number of times it's true divided by the number of times it's true plus the number of times its false so let's say you had a body of evidence and you can draw a lot of conclusions from this and been doing it for a while and not every like you get about nine out of ten times you get the Q find out you get the correct conclusion from this body of evidence the probability that your next conclusion you draw from that evidence is going to be true is 90% because you've got your past experience you're wrong about one out of every ten times so your next time it's probably going to be one out of every ten times Bayes theorem is the way of using this kind of ratio concept and again notice these are the same number there we go so from now on I'm actually I'm going to gray out that one little expression on the bottom because it's just a copy of the one on the top and now you understand what it's doing there we can ignore it and you won't be distracted by it from here on out that leaves two expressions to look at that you have to understand so it's getting easier we're getting there so what does that mean what is there was a thing up the up top mean what does that complex equation mean it in the sort of basic plain English sense or sort of pseudo English does kind of bastard math English hybrids a chimera it means this means that given all that we know given all the information that you have about the world about the physics about human nature about history about everything you know and all the evidence and so on the probability that our explanation is true the explanation we're testing equals how typical our explanation is in other words how typically is that fitting explanation of that kind of evidence times how expected the evidence is if our explanation is true so if you assume your explanation is true how likely is the evidence we have the kind of evidence we would have expected is it and then you divide that again you're repeating the above expression and you add that to how a typical our explanation is or in other words how typically are other explanations true rather than ours times how expected the evidence is if our explanation isn't true in other words we assume that some other if some other explanation is true not ours then how likely is the evidence we're looking at so you actually have to take into account alternative explanations of the evidence you can't ignore them so this idea of trying to start with a theory and if it fits the evidence and it must be true no no you also get a look at alternate theories of the evidence that see do they explain the evidence better and so this is how Bayes theorem takes that into account to give it a sort of visual idea and this is very rough approximation but this is the most intuitive way I could think of trying to make it seem but if you have like a scale a set of scales and on one side you have the hypothesis you're testing on the other side you have all of their hypotheses besides yours to explain a body of evidence and each end of that scale has two baskets on it one of those baskets you fill with eggs representing how typical your explanation is the other basket you fill with eggs representing how likely the evidence is and then do the same thing on the other side and you see where the scales fall I'm going to give you an example of this based on a UFO report story that really hit the press and national television made it all the way to the Larry King show in 2008 and basically the original reports that came out Stephenville that you're reading in the newspaper we're really sensationalist to like in to when you're reading maybe that this can't possibly be true this is like unbelievable and the story was that there are dozens in a Texas town dozens of independent eyewitnesses reported seeing a UFO a large silent object with bright lights Oh unfortunately the damn the text isn't translating okay I'll just tell you what it says and some saw some reported seeing fighter jets chasing it and witnesses said the object was a mile long and half a mile wide do you think if these are the actual things that they were seeing it's pretty incredible when you started looking at like the muon reports and so forth you look at the actual original reports that the press was sensationalizing it sounded a little bit more like what's in the middle here now this is not a picture of what was seen in Stephenville this is this is a picture of a meteorite breaking up in the atmosphere and so you have a multiple lights that are kind of in flying information really fast and they spread out over a large distance and they're changing color and these were all things that were reported about this strange object over Texas so the initial idea was it is this really sounded like a meteor breaking up in the atmosphere now if if we think of it that terms you think of prior probability and in past cases let's suppose we looked at all past cases where you saw these kinds of just krypton's and then you you saw where the investigation led and what it concluded and you found that nine out of ten times in past cases it turned out to be a meteor breaking up in the atmosphere if that's the case you have that background knowledge you've seen it happen nine out of ten times before that means the odds that this one is going to be just like that are also nine out of ten and so that's a 9 to 1 odds you put 9 eggs in one basket one and the other and you get heavily weighted towards the meteor explanation however we have a basket still to fiddle so we can't end the investigation there yes we can say well odds are that's what it is I don't know for sure until I do an investigation but if I can't do an investigation I really just have to settle on the fact 9 out of 10 odds it's a meteor so we know we know meteors often look a lot like that the way that these these witnesses are describing its spacecraft don't tend to look like that so much because they didn't actually describe the kinds of attributes that you would expect for a spacecraft the kinds of weird things they were describing the lights somewhat changing shape and color and distance and so forth sounds a lot more like meteors so I figure you know I don't know maybe spaceships could look like that aliens are weird right so let's suppose alien ships which suppose you know somehow magically that alien ships look like that half as often as meteors do now we know the probability has to be much less than that because alien ships would look much more like you know we've seen tons of eight movies in which alien ships are well conceived by the creators of those movies we know what people understand that alien ship would look like they don't normally look like these kinds of things but meteors look like them a lot so we do that we put the eggs in the basket we have a six to three eggs but that's again it's waiting even heavier towards meteor so now that we look at the evidence we consider the evidence in there we're getting it's getting even more heavily weighted towards meteor however if you do the mathematics if you do that properly without using the scales and trying to make eggs work out mathematically and so forth just do the math right using the equation you get a probability with those surface assumptions that I put in there you get a probability of ninety four point seven percent chance that it's meteors now that means it's about a one in 20 chance you're wrong now if you if you did this like like every hundred claims that means five times five of those claims are going to be false so you're going to be wrong a lot this is not a very high level of confidence to be in but it's high enough to be sure that you know it's not going to be aliens however if you get more information more evidence you can update the equation and you actually actually find out that it can actually over way even a high prior probability towards meteors it's nine to ten chances meteors but with enough evidence you can confirm it's not meteors it's something else now it turns out that the US government confirmed that at that time and location an aerial flare drop test had occurred and notice some of the other features of the reports were some saw fighter jets chasing them well no actually they were flying in front of them because they were dropping them and witnesses said the object was a mile long and half a mile wide which is about right for a for a flare drop system this happened the lights I have down here on the far on the far side here that's the Phoenix lights which is that created a similar UFO scare it looked like a Chevron shaped gigantic ship a mile wide floating slowly over Phoenix it turns out these are flares just floating you they are attached to parachutes they get dropped and they just float to illuminate the ground below so you know soldiers can fight in nighttime but it's just flares but your eye fills in they're just these separate lights floating free and but they're flying formation and they form a shape your brain fills in the shape and you see an actual physical triangular object in there so you see a spaceship but there isn't really one there so we've seen this kind of thing before and it turns out that military actually confirm that that's what happened so let's if we take this into account we get a lot of eggs stacked in the flares ba basket and we get a huge drop in that side this is the evidence that fills all of that basket we know that the location is an established Air Force training area where the people were seeing this thing happen that we knew that was an Air Force training area so that's actually known information the Air Force confirmed a flare drop test occurred there at that time that's pretty good evidence as well flare drops look exactly like that in fact that it the flare drops fit the evidence better than any other theory even meteors because meteors don't typically break up in the atmosphere they just you just see one streak but they do occasionally break up in the atmosphere and look like that but flares always look like that and then of course there's no other evidence of rose that supported meteors or aliens no satellite data no no radar data no ground invasions or anything like that so we got a huge head a huge hit towards flares so we started notice all the eggs in that Meteor basket there the prior probability is really heavily weighted towards meters but we put all that evidence and it changed everything and now that's where we are an important thing to point out through all of this so even though fast moving lights reports usually meters the evidence outweighs at by at least a thousand times if you do the math figuring le the odds that all these things in white boxes would occur for any other reason are so low that it's about a thousand times at least a thousand times probably more difference so if you if you were to work this out if we if we assume for example nine out of ten times its meteors that's that previous assumption and nine of the ten times it's not meteors its flares and you know let's be generous the rest of the time it's alien spaceships if this is if for its hypothetical and ships produce evidence like that half as often as meteors do like that assumption we had earlier but flares produce such evidence at least a thousand times more often than meteors do which we found when we looked at all the evidence afterwards then you get when you do the math you get these equations you get a 99% chance as flares a 1% chance it's meteors and a point zero zero six percent chance it's aliens which is about a 1 in 18,000 odds against aliens and remember we were being very generous if you were to adjust these percentages toward more realistic numbers those odds would go even further against aliens now this is this conclusion is intuitively obvious it's what you were already doing in your head throughout this process and how you would have done it I never even mentioned Bayes theorem and just presented the evidence it's basically the same conclusion would have come to on your own Bayes theorem simply explains why that's correct why you're thinking correctly when you do that as long as your intuition is balancing the probabilities according to Bayes theorem and knows what probabilities you're supposed to be balancing then your estimates of the odds that in any given explanation is true will be correct and it turns out this is true with any reasonable numbers at all like you can put almost any numbers into the equation as long as they're even remotely reasonable you're going to get pretty much every time the math the math is always going to work out to about a hundred percent chance it's flares and less than one an 18,000 chance it's aliens so that's that's the basic idea of how Bayes theorem works and what it's doing so let me go back and look at this complicated equation again what is going on here well I don't expect you done this is RIT but think of this is written in a foreign language these are foreign words you don't need to know with each word and letter means right off the bat it's just like you're trying to translate German and so let's say let's say this is a Russian it looks more like Russian right let's say or you know it looks like Sumerian actually ancient Sumerian so Buffy fans this is this is a spell okay so with that first with that first thing on in the equation is saying with that what expression there what that means is that's the probability that the claim H high H for hypothesis that's the probability that the claim H is true given the evidence E and their background knowledge be everything else you know about the world and E plus B that's all done all as you have so as all your information is in there given everything you know what is the probability that your hypothesis is true and you calculate that by running these other numbers in here and remember this is the same as above so we can ignore that and just concentrate on three numbers the prop the prior probability your hypothesis is true that probability based on past cases the likelihood of the evidence if your hypothesis is true so remember how well the evidence fits what you expect if your hypothesis is true and the likelihood of the evidence if some other hypothesis is true now you're saying you might notice there's another as a fourth number there that number is actually a derivation of the first number because remember if it's if it's nine and ten chance it's meteor's that automatically means it's a one in ten chance it's not so if you know the one prior probability you always know the other prior probability which means there's really only three numbers that you need to worry about and those are those three premises each one is a statement of probability and in fact you can take almost any argument that's been posed to you or any argument you make to anyone else and you can model it in Bayes theorem and you can actually model out like this is what I'm actually arguing and this is what you're actually arguing which means that every argument boils down to just three numbers and often if you're arguing with someone you will already agree with two of those numbers or you won't need to disagree even if you just do disagree it's not necessary to disagree which means it that boils any argument down to a dispute over just one you can then analyze the soundness of the case being made for that number and expose what's wrong with it or accept the conclusion and that's one of the most powerful uses for Bayes theorem as a rationalist now that's I've given you a sort of that's a really short basic tutorial on what base theorem is and what the equation means but I also want to talk to you about why you should care what's what's the use of this thing there are a lot of things once you understand base theorem like you once you get it you get that you wreak a moment you understand it and maybe you don't have that yet but if you keep pursuing looking into it you might get that get to that state it took me a while to get to that state as well but once you get there you start to learn all kinds of new things that really clever things about how correct reasoning works that you probably didn't think of before and that's one of the greatest uses for Bayes theorem and I'm going to give you a few examples these aren't all of them they're just some of the things you learn one of the things you learn is that that prior probability thing is always a relative probability not an absolute probability and this is a common mistake that people make and arguing and reasoning I run into it all the time when I argue about the resurrection with Christian apologists the resurrection of Jesus with Christian apologists I'll give an example in a moment the point here is it doesn't matter how rare meteors are for example when you go back to that example what matters is how often lights in the sky are meteors rather than aliens or something else it's the relative probability of all the possible explanations it's not the absolute probability that meteors occur so for example if 9 out of 10 times lights in the sky are meteors then even if the odds of seeing a meteor are one in a million the prior probability that lights in the sky are meteors is still 9 and 10 it the one in a million is irrelevant to this analysis so this is where we get to the resurrection argument to translate that it doesn't matter how improbable it is that the disciples of Jesus stole his body and lied about seeing him risen in order to promote their social reforms that seems like a wildly improbable hypothesis like that seems amazing like I probably could have only happened once in history once in history elevate it kind of did but doesn't matter what the odds of that are it could be one in a trillion against doesn't matter it's irrelevant what matters is how frequently religious fanatics do things like that relative to how often they actually witness corpses being reanimated if only one out of ten times bodies are actually reanimated notice that's an extremely generous assumption if only one out of every ten times bodies are actually reanimated and the rest of the time the claims of reanimation are bogus then the prior probability of reanimation is simply one in ten remember that earlier trilling - one against it's irrelevant we're at one in ten that's it that's your prior probability remember the Jain example it's just like that if one out of if you know if if any hundred people named Jane 99 or girls and one is a boy the probability that someone is a boy given that their name is Jane is simply one percent it doesn't matter how improbable it is to be named Jane in the first place as long as you know that first ratio that 99 to one ratio that's the only information you need now another thing you learn is that you don't need to know the actual frequencies or probabilities of anything for example you already know that whatever their actual frequency is of bodies being renamed corpses being reanimated whether it's zero whether it actually has a frequency it doesn't matter you don't need to know what it is you know it's certainly not higher than one in ten in fact you know it's not even one in ten because that would mean one of every ten missing bodies has risen from the dead and you know I play like I note here I play to hell with Social Security right you know how would we legislate for that I'm Greta lucky died you can even explore what that rate could possibly be can it be higher than one and 100 can it be higher than one in a million one in a billion given the evidence you know how what's the high great they would fit the evidence and there is some rate above which you are confident it's not that frequent anyway right no matter what the actual frequency is but you can explore where that boundary is and that's one of the useful things you can do with Bayes theorem you can also run the math for all kinds of different values just to see what it would take to convince you or what you would have to believe in order to believe any given conclusion so it actually shows you how to make your beliefs more consistent but you know ultimately you have to have good reasons for any number you settle on and usually you can settle on just that that's sort of its it has to be less than one in ten so anything you come out with one and ten you know it's going to be less than that so you can have those sort of uncertain probabilities and they work just fine so this is the thing like whatever a Christian insists that frequency is of missing bodies being reanimated they have to provide actual evidence that that is indeed the frequency because we have lots of evidence of religious fanatics lying hallucinating exaggerating so we know that behavior is frequent do we know reanimations are we do not so we don't have that kind of background knowledge that establishes that frequency never know for sure if it's it responding to me but remember that's just the prior probability even if corpse reanimation is extremely improbable good enough evidence can overcome that remember the flavor's example but you've hacked chat you have to actually have that evidence that's the real challenging part for Christian apologists now for those of you who want to hear the rest of this argument want to see a Bayesian or the conclusive Bayesian argument against the resurrection of Jesus which actually answers all the attempts to use Bayes theorem to prove Jesus rose from the dead yes Christians have tried that if you want to see the ultimate argument for that that's in my book and the book Christian delusion edited by John Loftus I have two chapters in that one of them is you know my definitive chapter on the resurrection which is all in plain English but if you look in the endnotes it translates those things into the Bayesian terms and so you can see how Bayes theorem translates between ordinary reasoning to ordinary descriptions of reasoning and naught but I've also got in in this book the end of Christianity which is being sold today in fact I only get money from this book if you buy here so by the end of Christianity afraid especially for interested in Bayes theorem because I have three chapters in this one is my foundational moral theory chapter but the other two are Bayes theorem applying Bayes theorem to things and one of them proves that the whole religion the whole Christian religion is false just based on how it began and not just the resurrection but a lot of other facts you'll find a lot really interesting things in there interesting facts but you'll also see how Bayes in an analysis can lead you to understand the sort of things that you felt intuitively but couldn't articulate as an argument based through it makes it slam-dunk and it turns it into an argument over numbers which you get to the point where they can't dispute those numbers anymore because you're being so generous to their side and yours yet you're still getting the conclusion that you're arguing for they can't tweak the numbers any further without looking completely ridiculous and I also in this book have a chapter applying Bayes theorem to the design argument I apply to evolution versus creationism I apply to the biogenesis argument but I also apply it to cosmology in a show and it's interesting that I'm not the one who did this but I just put in lay terms but I show that the fine-tuning of the physical constants the fine-tuning argument which many of you probably heard many a time actually proves that God does not exist now you might think how the hell do you do that well read that chapter and like I said this isn't me this was there were actually two teams of mathematicians independently of each other not even knowing about each other's work came up with the same Bayesian conclusion using the same Bayesian analysis proving this point I cite them in the chapter and I explained in plain English what it is that they discover and why they're right and they are right so that if you want to see and those chapters also really explain more Bayes theorem they give you examples of applications of base theorems if you want and early you know glimpse of trying to learn more about Bayes this is the thing I also reference more books you could read about Bayes in these chapters as well but if you want the definitive my definitive book explaining Bayes theorem and how to apply it I'm my book proving history Bayes theorem and the quest for the historical Jesus is done it's gone through peer review it is now slated for release in April of 2012 and I've made a really concerted effort to make this intelligible to historians and humanities majors so it's not it doesn't depend on you being a mathematician to understand it and that gives you a lot of examples and also sets up the stage for arguing that Jesus didn't really exist this book doesn't argue that specifically but it does show that all the arguments for his existence are based on faulty logic using Bayes theorem now the things you learn which I'm not going to go into or the extraordinary claims require extraordinary evidence Bayes theorem actually proves that this is correct absence of evidence sometimes is evidence of absence and in fact Bayes theorem tells you when it is and when it isn't so let's give you an example of that last one that you all just read we'll start with this particular argument and many of you may know this if you don't know this you should because this is 300 BC Epicurus made this argument and it's kind of definitive I mean really the arguments done like walk away like why they keep going I don't know but is God willing to stop evil but unable then he is not powerful is he able but unwilling then he is not good is he both able and willing then how can there be evil is he neither able nor willing then why calling God let me give an example I'll just take one example American slavery I don't have time to retell its horrors just to spice it to say it's one of the most evil things we ever did couldn't God have just given southern whites a revelation explaining this I mean it's super easy to do just think of the hundreds of years of lives that could have been changed for the better just think of how Africa what state it would be and now had it not been ravaged by slavery it's just that it goes it boggles the mind the way the world would be a better place just from just a booming voice from heaven and a light hey that slavery thing that's up stop that I don't approve it easy easy peasy the Bible doesn't say one word against slavery in fact it explicitly explicitly codifies it along with the ten commandments as part of God's law and it treats and it treats slavery as a moral norm throughout even the New Testament even Jesus treats it as a moral norm so this is really a slam dunk argument I mean this is this is clear-cut I mean the odds that God would not speak out against this absolute unbelievable horror especially to people who are devotedly supposedly following him or certainly want to follow him any of them probably did I mean it came on it's obvious this is this is a no-brainer so you know we could win the argument just on that alone but you know what about that assload of excuses believers always wheel out at this point and that's where oh by the way also known as making up but that's where we get to this question ad hoc reasoning making up excuses to explain anyway so you can pick any hypothesis and you can make it fit any body of evidence by making up any number of excuses that you need to make that happen now let's suppose you make up an excuse for Godman you don't have any evidence that he has that excuse you're just here to saying he could have that excuse and let's say you don't have any evidence against him having that excuse either so you really it's either 50/50 you you really don't know could go either way now that means that so far as you know it's as likely as not a straight-up 50/50 chance and if you do that you diagram it you have like this circle here represents all the hypotheses all the god hypothesis you can think of half of them in frequency half of them have this excuse and half don't which means that the probability of their hypothesis being true with that excuse equals half the prior probability of your hypothesis . so coming up with these kinds of excuses halves things and this is how it works if the probability that God exists given your background knowledge equals 0.5 now this is extremely generous I'm just saying that you have no evidence against God's existence and you have no enough evidence for you just like you just somehow you know giant rubber tire machine just popped out a sentient being in he has never has no prior background knowledge at all and someone evangelist comes up to him talking about God so he has no idea no prior information well it's 5050 right notice I'm being extremely generous and there's a 50/50 chance that God has the excute assumed excuse so you have this sort of randomly created person and then this evangelist comes up and he says God he says well there's a southern guy talking about slavery and that sounds pretty up and it kind of contradicts your hypothesis this is oh well has that he could have this excuse 50-50 chance well that means you the prior probability that you're special God exists given your background knowledge is now 0.25 because that's half of that because the other point two-five is taken up by gods that don't have that excuse so the probability of God exists becomes half as likely you see where this is going now that's for a case where it's 50/50 no evidence for or against God having that excuse what if it's an unlikely excuse the dog ate my homework I couldn't have done it I was dead at the time claims that are almost never true and you actually have some evidence that they're probably not true let's say let's get let's say an example like one in a hundred times I say like the dog ate my homework let's say we did a research study this would be an interesting one to do so you did research study and found that one out of every time a hundred times that kids say the dog ate my homework a dog really ate his homework and that's being generous on you know one out a hundred I don't know but let's say that were the case that would mean that the probability that the prior probability that that excuse is true is a hundred times less so now you've got the probability of our hypothesis with that excuse times one over 100 so it's getting really really small it's a hundred times smaller so again let's say the prior probability probability that God exists some God exists is 0.5 5050 and there's at best a 1% chance that God has the assumed excuse then the prior probability that your special God exists given your background knowledge is point zero zero five in other words the probability that God exists the prior probability that God exists becomes a hundred times less likely so the more desperate your excuse you're actually hurting your case you're actually making your hypothesis less probable so put it another way if if benevolent gods exist at all nice gods without that excuse are a hundred times more likely at least why because they're far easier to imagine and far simpler to produce just don't add that one extra annoying thing and presto better God so what's the probability that a benevolent God would have a valid excuse not to say one heavenly word to his devoted believers throughout the entire history of American slavery and what evidence do you have supporting that probability now when we look at it we see we've looked from our background knowledge of benevolent beings and we know a lot of them where we've met them they're called human beings of all benevolent beings that we've seen in action and have knowledge about who have the power to speak unharmed they know they can't be harmed for for saying speaking their mind of all of those people who put them together throughout history documenting a valid excuse not to speak up against slavery is so rare we never see one instance of it or certainly there is one it's extraordinarily rare rare means infrequent very infrequent which means very improbable so in fact it's got to be at least one in a million like you have like a million benevolent human beings who could speak out against slavery without being harmed for it how many of them would you think would have a valid excuse not to speak and less than one in a million I think all of them would have a valid reason to speak and not have a valid excuse not to so that means that less than one in a million benevolent beings with the ability to speak unharmed have a valid excuse not to at best now that means that the prior probability that your God exists becomes a million times less likely so as soon as you start coming up with these trying to come up with these excuses you're actually making your hypothesis wildly improbable and this is what Occam's razor does under Bayes theorem another way to put it again if benevolent gods exists at all then nice gods without that excuse are actually a million times more likely and why because we already observe that benevolent beings without that excuse or a million times more likely we have concrete empirical proof of that so it's not even a conceptual idea so not even abstract we have concrete empirical evidence confirming this and they can't gain say it now if you put this into the equation the thing working out the equation remember those three numbers I talked about all breaks down to three numbers and in the equation that I'm talking about this in this model I'm talking about this number down here is already a hundred percent that God would not speak out is a hundred percent guaranteed if there is no God I mean you know that's an easy equation to figure out right so they want they want this number to be a hundred percent in order to compete but doing that reduces this number by a million so this top equation becomes this bottom equation we start out with odds of God existing of less than one and a half million and this is a strong empirical case that I mean when I say less than one and a half million I mean that's a good argument that we have the odds are less than that so really when people say that we don't have evidence against God's existence that's false we have really really really good evidence that God it does not exist so when you have this if you started out with odds of less than one and one and a half million they put their excuse in to get that number that one percentage up end result is exactly the same odds exactly the same on so they got nowhere their excuse is useless it doesn't it change the equation at all that means that no amount of excuses can change the fact that on present evidence a living in benevolent God is simply improbable and that's an example of how Bayes theorem can be extremely useful to you in making this point because any you could get down to arguing about those three numbers and they just can't they can't argue about those three numbers and because those three numbers the equation works out they can't say that math is wrong you know it's like I deny all math and logic now okay well we know how that's going to work out for you so you can read all about that you can read all about that in april of year 40 29 ad but those of you who don't want to wait around that long my books coming out in April of 2012 now I don't I don't do the disproving God thing in there but I do do a lot of stuff that's very useful for understanding Bayes theorem and applying it to history which is broadly applicable not just to religious claims in history and if you want if you want something now like you're really eager to start learning more about Bayes theorem and see how powerful it is and how useful it is and how it can be applied in your lives skeptically this book the end of Christianity which is available here today so and this is right now this is my primary source of income is selling these books so if you want to support my work please buy and I'll sign anything that you get so come up out to me and I'll sign it and or you know whatever I'll spy any of my other books I have many useful books and that's it thank you [Applause]

Channel: HamboneProductions

Views: 120,911

Rating: 4.7988167 out of 5

Keywords: richard, Carrier, skepticon, hambone, productions, bayes, theorom, atheist, there, is, no, god, proof, ssa, humanist, jeff, foxworthy, Atheism, Darwin, Dawkins, Creationism, Design, Humanism, Intelligent, Truth

Id: HHIz-gR4xHo

Length: 47min 22sec (2842 seconds)

Published: Fri Nov 25 2011

Reddit Comments

[u/[deleted]]

Great video. I enjoyed the point that the more the fundies argue the farther behind they get.

[u/psycadelia]

Very interesting

[u/bigwhale]

Carrier is the master of slides.