tag:blogger.com,1999:blog-6894866515532737257.comments2014-11-03T04:01:32.669-08:00Probably Overthinking ItAllen Downeyhttps://plus.google.com/111942648516576371054noreply@blogger.comBlogger405125tag:blogger.com,1999:blog-6894866515532737257.post-67098678678791887352014-10-10T14:41:55.787-07:002014-10-10T14:41:55.787-07:00If only authors and publishers would meet the spir...If only authors and publishers would meet the spirit of the discipline of scholarship as well as its letter. Just make a bibliography. I have a small collection of memorable and valuable bibliographies that I've copied. Some authors divide up their bibliographies into useful segments (for example, by primary/secondary sources, periodicals, etc.). Those authors have my deepest gratitude (and respect).Edward Carneyhttp://www.blogger.com/profile/18390516990905425802noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-51907379381499695292014-10-08T19:33:31.527-07:002014-10-08T19:33:31.527-07:00Tree diagram
1st 2nd P
GFB 1/2*x*1/2=...Tree diagram<br /> 1st 2nd P<br /> GFB 1/2*x*1/2=1/4*x<br /> GF GFGF 1/2*x*1/2*x=1/4x^2<br /> GFG 1/2*x*(1-x)*1/2=1/4*(x-x^2)<br />G<br /> GB 1/2*(1-x)*1/2=1/4*(1-x)<br /> G GGF 1/2*(1-x)*x*1/2=1/4*(x-x^2)<br /> GG 1/2*(1-x)*(1-x)*1/2=1/4*(1-x)^2<br /><br /> BB 1/2*1/2=1/4<br />B BGF 1/2*1/2*x<br /> BG 1/2*1/2*(1-x)<br /><br />Sample space={GFB, GFGF, GFG, GGF, BGF}<br /><br />P(GFGF)+P(GFG)+P(GGF)=(1/4*x^2+1/4*(x-x^2)+1/4*(x-x^2))/(1/4*x+1/4*x^2+1/4*(x-x^2)+1/4*(x-x^2)+1/4*x)=(2-x)/(4-x)<br /><br />When x->0 (Florida is rare name) P=(2-0)/4-0=1/2<br />when x->1/2 (Florida is a half) P=(2-1/2)/(4-1/2)=3/7<br />when x->1 (Florida is equivalent of girl definition) P=(2-1)/(4-1)=1/3 (reduction to Problem 2) Vasili Gavrilovhttp://www.blogger.com/profile/13021023056247394884noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-5034894005494021692014-10-07T07:29:21.970-07:002014-10-07T07:29:21.970-07:00Good point. I would love to see some kind of typo...Good point. I would love to see some kind of typographical distinction between GD endnotes that contain VIAI and the ones that just have BI (bibliographical information).Allen Downeyhttp://www.blogger.com/profile/01633071333405221858noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-4600013712175887202014-10-07T01:51:04.270-07:002014-10-07T01:51:04.270-07:00Thanks! However, I would be happy already if the a...Thanks! However, I would be happy already if the authors would write only the necessary, but (for most readers) uninteresting information in the goddamn endnotes, like bibliographical or legal stuff. I hate to always keep a finger or a second bookmark, which must be synchronized with the primary bookmark, in the last few pages, just because the author didn't know how to fit the very interesting additional information (VIAI) or further explications in the main text.Grüner Gimpelhttp://www.blogger.com/profile/14167074815990880487noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-87762797839331274512014-10-01T07:52:53.966-07:002014-10-01T07:52:53.966-07:00Very interesting, Allen. I think you are in the ri...Very interesting, Allen. I think you are in the right ballpark and are right about the linear relationship between the rate (speed) and time. Another way to think about it is that it's a common negative exponential growth curve when marathon time (or percentage change in speed) is on the y-axis. Doing it that way makes it clear that we are running up against a limit at some point.DShttp://www.blogger.com/profile/18277161652273613847noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-44376154112716734392014-09-16T05:19:31.335-07:002014-09-16T05:19:31.335-07:00Thanks for these comments, and for your kind words...Thanks for these comments, and for your kind words. You asked how what I presented differs from most intro stats classes. Based on the textbooks I've seen, I get the impression that many stats classes teach hypothesis testing as a cookbook process, so students learn how to perform various tests and when to use which test. I have not seen much emphasis on the sampling distribution as the basis for standard error and confidence interval (but I am sure there are example of books and classes that do).<br /><br />About the computational approach, you suggested that students might learn how to use tools, but not how they work. I don't think the computational approach prevents students from learning both, and compared to the standard mathematical approaches, it provides a lot of flexibility: students can learn how to use a black box, then learn how it works (a top-down approach) or start with building blocks and assemble the black box (bottom-up).Allen Downeyhttp://www.blogger.com/profile/01633071333405221858noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-85992281170713542152014-09-16T04:45:08.994-07:002014-09-16T04:45:08.994-07:00I am pleased to discover that the approach I provi...I am pleased to discover that the approach I provided on Reddit (as username blippage) was basically sound. Phew. It's good to know that time hasn't totally withered away my reasoning ability.<br /><br />When you say "The approach I presented here is a bit different from what's presented in most introductory stats classes", how so? Isn't there only one basic way to solve this problem: namely, by understanding that the mean of the sum/difference of two normally distributed variables is the sum/differences of the means, and the variance is the sum of the variance?<br /><br />Also, don't you think that there is a danger that by approaching problems programmatically, it is teaching students to think like engineers rather than mathematicians; that is to say, "I know that it does work, but I'm not sure why". Having said that, an approach that is "too" mathematical can end up looking like symbols just being pushed around the page, with the underlying concepts lost in the process.<br /><br />Your Think books look really interesting, and I think I owe it to myself to read them.<br /><br />All the best, Professor. You're doing a great job of educating the general public about statistical ideas.Max Powerhttp://www.blogger.com/profile/04470463426170671630noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-80834503611950525192014-09-15T05:12:16.891-07:002014-09-15T05:12:16.891-07:00I think he only had a few slides, but he has links...I think he only had a few slides, but he has links to the data and the code. Most of his presentation was a live tutorial.Allen Downeyhttp://www.blogger.com/profile/01633071333405221858noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-54905307478450521172014-09-15T05:06:57.011-07:002014-09-15T05:06:57.011-07:00Thx for this article with the links. Is Imran Male...Thx for this article with the links. Is Imran Malek's presentation that short? Only 10 slides?rihttp://www.blogger.com/profile/04354229352442247479noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-70647313124428826062014-08-31T17:47:08.275-07:002014-08-31T17:47:08.275-07:00I'm glad you point out this fallacious use of ...I'm glad you point out this fallacious use of p-values. What bothers me, though, is that people fail to dub erroneous p-values the pretend p-values that they are. In order for a p-value, say of .05, to be an actual and not merely a nominal (computed or pretend) p-value, it's required that<br />Prob(p-value < .05; Ho) ~ .05.<br /><br />With the multiple testing in the jelly bean case, say, the probability of so impressive-seeming a p-value is ~.65. I will look carefully at the paper you cite, but I just wanted to note this because it drives me crazy when pretend p-values aren't immediately called out for what they are.<br /><br />thank you for bringing out the fallacy of failing to adjust p-values.<br />errorstatistics.comMAYO:ERRORSTAThttp://www.blogger.com/profile/02967648219914411407noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-20324714342431412882014-08-29T20:38:49.005-07:002014-08-29T20:38:49.005-07:00that xkcd is a perfect analogy to the Hooker paper...that xkcd is a perfect analogy to the Hooker paper nonsense. Thanks you for clarifying it!Cigal MDhttp://www.blogger.com/profile/14389394263265656420noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-59486269738838730972014-08-29T20:31:43.171-07:002014-08-29T20:31:43.171-07:00Thank you for this simple, useful explanation. I a...Thank you for this simple, useful explanation. I appreciate it. Dorit Reisshttp://www.blogger.com/profile/05606807832521443462noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-45516013698174626722014-08-21T06:43:39.037-07:002014-08-21T06:43:39.037-07:00Ok I see, total probability..., thanks!Ok I see, total probability..., thanks!Henrihttp://www.blogger.com/profile/00434803886040541009noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-11022125910069703392014-08-19T11:13:41.387-07:002014-08-19T11:13:41.387-07:00I plugged the previous values into Bayes's the...I plugged the previous values into Bayes's theorem:<br /><br />P(A|E) = P(A) P(E|A) / P(E)<br /><br />Where the denominator P(E) is<br /><br />P(A) P(E|A) + P(B) P(E|B)<br /><br />All clear?Allen Downeyhttp://www.blogger.com/profile/01633071333405221858noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-18779576131694968062014-08-16T23:50:06.950-07:002014-08-16T23:50:06.950-07:00Hi Allen.
In 3), you end up with:
P(A|E) = 8/54 ~...Hi Allen.<br /><br />In 3), you end up with:<br />P(A|E) = 8/54 ~ 0.15.<br /><br />How do you determine that P(E) = 0.54 ?<br />Henrihttp://www.blogger.com/profile/00434803886040541009noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-32602316813430833482014-08-14T07:00:04.545-07:002014-08-14T07:00:04.545-07:00That's cool. Do you have your R code on GitHu...That's cool. Do you have your R code on GitHub or some other public repo? I think others would like to see it. Let me know and I will add a link to it.Allen Downeyhttp://www.blogger.com/profile/01633071333405221858noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-87632892469646090692014-08-07T13:10:10.487-07:002014-08-07T13:10:10.487-07:00I was thinking along those lines. So, essentially,...I was thinking along those lines. So, essentially, we say (in R, sorry)<br />x <- seq(from=14000,to=64400,by=700)<br />Like <- dnorm(x-20000,mean=0,sd=sd(SC1Diff))<br />Prior <- approx(SC1PDF$x, SC1PDF$y, x)<br />Post <- Prior$y*Like<br />Post <- Post /sum(Post)<br />where SC1PDF is the kernel density approximation to the data sets. I do indeed match your chart in the book. Thanks! Love the book, but I'm translating it into R as I go, rather than using the Python framework, so it's just a bit tougher. Appreciate it.Reuben Gannhttp://www.blogger.com/profile/14813246182350894222noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-55135986885358915062014-08-07T12:54:40.606-07:002014-08-07T12:54:40.606-07:00Hi Reuben,
P(20000 | H_x) is the probability that...Hi Reuben,<br /><br />P(20000 | H_x) is the probability that you guess 20000, given that the actual value is x, so that's the same as the probability that diff is (x-20000). We can't really compute that probability, but we can compute a density proportional to that probability by evaluating the PDF of diff at (x-20000).<br /><br />Does that make sense?Allen Downeyhttp://www.blogger.com/profile/01633071333405221858noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-76739150248983838112014-08-07T12:47:31.294-07:002014-08-07T12:47:31.294-07:00My understanding is getting lost in the Python. Yo...My understanding is getting lost in the Python. You are trying to compute the posterior for "E = my guess is 20000", where<br /><br />P(H_x | 20000) = P(20000 | H_x) P(H_x) / P(20000),<br /><br />where x=0..75000 and H_x means the price is x, correct? You assume that the distribution of errors is proportional to e^{-x^2/2 sigma^2}, where sigma is the standard deviation of diff (which is $6899.91 for Showcase 1). But what is P(20000 | H_x)?Reuben Gannhttp://www.blogger.com/profile/14813246182350894222noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-62038576570127154262014-08-07T12:44:31.220-07:002014-08-07T12:44:31.220-07:00Very interesting. Thanks for this comment!Very interesting. Thanks for this comment!Allen Downeyhttp://www.blogger.com/profile/01633071333405221858noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-59091846791029176262014-08-05T15:28:12.997-07:002014-08-05T15:28:12.997-07:00Greetings Professor. Thanks you for your kind resp...Greetings Professor. Thanks you for your kind responses on a different thread. I couldn't help but comment on this one, too.<br /><br />I am mostly interested in statistics coming at it from the point of view of investing in the stock market. There are many factors that a investor could choose from when selecting a share to buy, and of course one must try to winnow these factors out.<br /><br />Although "Efficient Market" theory has been around for some time, there has been emerging interest in psychology on people's investing decisions. The dominant faction are what we might call the "behaviouralists". I call these the "glass half empty" guys, as there central thesis is that human beings are inherently irrational. So they are subject to such things as "hindsight bias", "confirmation bias", "base rate fallacies", and many many more. Particularly relevant here, though, is "conservatism" (they underweigh new sample evidence when compared to Bayesian belief-revision) and conflating correlation with causation. So, if you believe this school of thought, then human beings are big bags of irrationality.<br /><br />On the other hand, psychologist Gigerenzer has studied the use of "bounded rationatily and heuristics in decision making". His work seems almost diammetrically opposed to the behaviorilists. He is a "glass half full" guy, and he makes a good demonstration of how humans are actually capable of making good decisions under uncertainty. He showed that under some circumstances, simple heuristics can often beat statistical methods, often because the latter tends to over-fit to training data.<br /><br />Your post neatly highlights the contrasts between the two camps. Your article neatly demonstrates how an apparent irrationality can actually be rational.<br /><br />In a way, it's quite remarkable when you think about it: Mother Nature is trying to endow humans with optimal survival decision-making skills for a lack of carefully tabulated statistics tables. I wonder just how much of irrational human behaviour will later be found out to be best-fit adaptationally to our environment.<br /><br />I hope this has been interesting.Max Powerhttp://www.blogger.com/profile/04470463426170671630noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-56044562379207446382014-08-05T13:38:02.435-07:002014-08-05T13:38:02.435-07:00Don't worry about overposting, but as some poi...Don't worry about overposting, but as some point I might have to stop overreplying :)<br /><br />Reading between the lines, I think you are coming face to face with one of the central issues of Bayesian inference, which is how to interpret probabilities, and especially the prior probability.<br /><br />In this case, P(H) is the prior probability that I am in the right class. If I chose the classroom at random, P(H) would be low. But I am basing my solution on the assumption that I did not choose the classroom at random, but rather tried to go to the right place. And based on my prior experience with navigating unfamiliar campuses, I estimate that my chance of being in the right place is about 90%.<br /><br />In frequentist terms, you could say that the relevant sample space is "all the times I've tried to find the right room", rather than "all the classrooms on campus."<br /><br />In (subjective) Bayesian terms, you would say that 90% is my subjective degree of belief that I am in the right place, based on relevant background information.<br /><br />But I would not say (as I think you did) that I am making a claim about the university, or that my Downeyian university is very different from a real university. My analysis is based on a model and the simplifications that come with it, but I don't think the model is as weird as you suggest.<br /><br />Thanks for this line of questions; I think it is productive.Allen Downeyhttp://www.blogger.com/profile/01633071333405221858noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-11267127084434500202014-08-05T13:25:53.857-07:002014-08-05T13:25:53.857-07:00Many thanks for your replies, professor. I hope I&...Many thanks for your replies, professor. I hope I'm not overposting.<br /><br />Would it be fair to say that my misconception of the problem is that I'm taking Olin University as the population; whereas I should be considering the population not as the Olin University, but a "Downeyian University". <br /><br />The Downeyian University is a special university ... one in which you have a 90% chance of turning up to the right class ... and not the Olin University, in which you would have only a very small chance of turning up at the right class if you just chose one at random.<br /><br />And this Downeyian University is a very strange University indeed ... because although you can specify what classes you are likely to turn up correctly for (it's the ones that you teach), you don't know what classes constitute the incorrect choices. They will be some proper subset of the entire Olin University, but we don't know what. The only thing we can say about it is that there is the same proportion of males as females. That would presumably be an assumption.<br /><br />But there's more! Although you're assuming that proportion of incorrectly chosen, you might be wrong. In fact, it's even plausible. How? Well, suppose you mostly gives lectures in the science faculty. Suppose that the science students are 90% male - not 50% male - and exactly the same proportion as your own class. What happens then, of course, is that the presence of females would actually give you no information.<br /><br />And maybe the situation is even worse than that! Maybe the actual "Downeyian" population contains more than 90% males, but that the males have a disproportionately larger distaste for mathematics and programming. Maybe they prefer engineering, or something. In that case, your intuition would have to be entirely flipped around ... the presence of females would be a positive indication that you're actually in the right class.<br /><br />Or perhaps I've got the wrong end of the stick. But I think that what I'm saying makes sense.<br /><br />Who would have thought statistics could be so much fun? ;)Max Powerhttp://www.blogger.com/profile/04470463426170671630noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-35677542581454940972014-08-05T12:17:37.645-07:002014-08-05T12:17:37.645-07:00Ah, now I see the problem! My previous reply was ...Ah, now I see the problem! My previous reply was wrong, but the numbers in the article are correct (but explained badly).<br /><br />As you said, the denominator P(F) should be P(F|H) P(H) + P(F|-H) P(-H), which is 0.14, not 0.5, and that yields P(H|F) = 0.64.<br /><br />In your first message, you objected to this denominator because you said it assumes that my class makes up 90% of the population of students. I think that's not right -- rather it takes into account that I am initially 90% sure that I am in the right class. But the term P(F|H) = 0.5 assumes (as you suggest) that my class is an insignificant part of the student population.<br /><br />Sorry for my confusion, and thanks for pointing this out. When I have a chance, I will edit the article to clarify.Allen Downeyhttp://www.blogger.com/profile/01633071333405221858noreply@blogger.comtag:blogger.com,1999:blog-6894866515532737257.post-34299538050538101042014-08-05T11:43:10.780-07:002014-08-05T11:43:10.780-07:00But if you take P(H) = 0.9, P(F|H) = 0.1, P(F) = 0...But if you take P(H) = 0.9, P(F|H) = 0.1, P(F) = 0.5 and plug it into the formula, you get<br />P(H|F) = P(H) * P(F|H) / P(F)<br />= 0.9 * 0.1 / 0.5 = 0.18<br />which is not the answer of 0.64 that you gave in your post.Max Powerhttp://www.blogger.com/profile/04470463426170671630noreply@blogger.com