andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1816 knowledge-graph by maker-knowledge-mining
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Introduction: Joe Blitztein sent around the following graph: (The x-axis goes from 2000 to 2012 and the y=axis goes from 0 to 120.) 100 statistics majors (this combines sophomores, juniors, and seniors, but still, that’s a lot more than the 1 or 2 or 3 a year we’re used to seeing). At first I was like, whoa! But then I thought, why not 100 or even 200 or 300 statistics majors? Statistics is important in itself, it’s relatively easy as far as quantitative majors go, it’s applicable to lots of other areas. The real question should be not, What’s been happening that’s made statistics so trendy lately? but rather, What took so long for this to happen, and why isn’t statistics more popular? Both places where I studied as an undergraduate, statistics was just a subset of the math department, and maybe the only reason I ended up in statistics is that I took a probability course one semester because, at 5pm, it fit my schedule.
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1 Joe Blitztein sent around the following graph: (The x-axis goes from 2000 to 2012 and the y=axis goes from 0 to 120. [sent-1, score-0.538]
2 ) 100 statistics majors (this combines sophomores, juniors, and seniors, but still, that’s a lot more than the 1 or 2 or 3 a year we’re used to seeing). [sent-2, score-1.235]
3 But then I thought, why not 100 or even 200 or 300 statistics majors? [sent-4, score-0.321]
4 Statistics is important in itself, it’s relatively easy as far as quantitative majors go, it’s applicable to lots of other areas. [sent-5, score-1.245]
5 The real question should be not, What’s been happening that’s made statistics so trendy lately? [sent-6, score-0.796]
6 but rather, What took so long for this to happen, and why isn’t statistics more popular? [sent-7, score-0.56]
7 Both places where I studied as an undergraduate, statistics was just a subset of the math department, and maybe the only reason I ended up in statistics is that I took a probability course one semester because, at 5pm, it fit my schedule. [sent-8, score-1.867]
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same-blog 1 1.0 1816 andrew gelman stats-2013-04-21-Exponential increase in the number of stat majors
Introduction: Joe Blitztein sent around the following graph: (The x-axis goes from 2000 to 2012 and the y=axis goes from 0 to 120.) 100 statistics majors (this combines sophomores, juniors, and seniors, but still, that’s a lot more than the 1 or 2 or 3 a year we’re used to seeing). At first I was like, whoa! But then I thought, why not 100 or even 200 or 300 statistics majors? Statistics is important in itself, it’s relatively easy as far as quantitative majors go, it’s applicable to lots of other areas. The real question should be not, What’s been happening that’s made statistics so trendy lately? but rather, What took so long for this to happen, and why isn’t statistics more popular? Both places where I studied as an undergraduate, statistics was just a subset of the math department, and maybe the only reason I ended up in statistics is that I took a probability course one semester because, at 5pm, it fit my schedule.
Introduction: I know next to nothing about golf. My mini-golf scores typically approach the maximum of 7 per hole, and I’ve never actually played macro-golf. I did publish a paper on golf once ( A Probability Model for Golf Putting , with Deb Nolan), but it’s not so rare for people to publish papers on topics they know nothing about. Those who can’t, research. But I certainly have the ability to post other people’s ideas. Charles Murray writes: I [Murray] am playing around with the likelihood of Tiger Woods breaking Nicklaus’s record in the Majors. I’ve already gone on record two years ago with the reason why he won’t, but now I’m looking at it from a non-psychological perspective. Given the history of the majors, what how far above the average _for other great golfers_ does Tiger have to perform? Here’s the procedure I’ve been working on: 1. For all golfers who have won at at least one major since 1934 (the year the Masters began), create 120 lines: one for each Major for each year f
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Introduction: I studied math and physics at MIT. To be more precise, I started in math as default–ever since I was two years old, I’ve thought of myself as a mathematician, and I always did well in math class, so it seemed like a natural fit. But I was concerned. In high school I’d been in the U.S. Mathematical Olympiad training program, and there I’d met kids who were clearly much much better at math than I was. In retrospect, I don’t think I was as bad as I’d thought at the time: there were 24 kids in the program, and I was probably around #20, if that, but I think a lot of the other kids had more practice working on “math olympiad”-type problems. Maybe I was really something like the tenth-best in the group. Tenth-best or twentieth-best, whatever it was, I reached a crisis of confidence around my sophomore or junior year in college. At MIT, I started right off taking advanced math classes, and somewhere along the way I realized I wasn’t seeing the big picture. I was able to do the homework pr
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Introduction: See more at the Statistics Forum (of course).
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Introduction: John Cook noticed something : I [Cook] was looking at the preface of an old statistics book and read this: The Bayesian techniques occur at the end of each chapter; therefore they can be omitted if time does not permit their inclusion. This approach is typical. Many textbooks present frequentist statistics with a little Bayesian statistics at the end of each section or at the end of the book. There are a couple ways to look at that. One is simply that Bayesian methods are optional. They must not be that important or they’d get more space. The author even recommends dropping them if pressed for time. Another way to look at this is that Bayesian statistics must be simpler than frequentist statistics since the Bayesian approach to each task requires fewer pages. My reaction: Classical statistics is all about summarizing the data. Bayesian statistics is data + prior information. On those grounds alone, Bayes is more complicated, and it makes sense to do classical sta
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Introduction: Joe Blitztein sent around the following graph: (The x-axis goes from 2000 to 2012 and the y=axis goes from 0 to 120.) 100 statistics majors (this combines sophomores, juniors, and seniors, but still, that’s a lot more than the 1 or 2 or 3 a year we’re used to seeing). At first I was like, whoa! But then I thought, why not 100 or even 200 or 300 statistics majors? Statistics is important in itself, it’s relatively easy as far as quantitative majors go, it’s applicable to lots of other areas. The real question should be not, What’s been happening that’s made statistics so trendy lately? but rather, What took so long for this to happen, and why isn’t statistics more popular? Both places where I studied as an undergraduate, statistics was just a subset of the math department, and maybe the only reason I ended up in statistics is that I took a probability course one semester because, at 5pm, it fit my schedule.
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Introduction: “Ethics and Statistics” is descriptive but boring. It sounds like the textbook for a course which, unfortunately, nobody will take. “Lies, Damn Lies, and Statistics” is too unoriginal. “How to Lie, Cheat, and Steal With Statistics” is kind of ok, maybe? “Statistical Dilemmas”: maybe a bit too boring as well. “Knaves and Frauds of Statistics, and Some Guys Who’ve Skated a Bit Close to the Edge”: Hmmm…. Maybe we have to get “statistics” out of the title altogether? “Knaves and Frauds of Data Science”? “Date Science and Data Fraud”? “10 Things You Really Really Really Shouldn’t Do With Numbers”? And, if no better idea comes along, there’s always “Evilicious: Why We Evolved a Taste for Being Bad.” (Regular readers will know what I’m talking about here; the rest of you can google it.) Or maybe just “The Wegman Report”? It’s hard to come up with a good title. Even John Updike had difficulties in this regard. If any of you can suggest a better title for my eth
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Introduction: Here (from the Annals of Applied Statistics ). “Thus, arguably, all of Section 3 is wrong until proven otherwise.” As with retractions in general, it makes me wonder about the rest of this guy’s work. Dr. Anil Potti would be pooping in his pants spinning in his retirement .
Introduction: Dikran Karagueuzian writes: I am in the process of choosing a textbook for a junior- or senior-level undergraduate two-semester sequence in probability and statistics. I would be obliged if you could recommend one which is free (or at least cheap), or inquire with your blog readers for such a recommendation. The course has been taught successfully in the past using Mathematical Statistics with Applications, by Wackerly, Mendenhall, and Scheaffer. Also at roughly the right level is Probability and Statistics by deGroot and Schervish. However, the current edition of the first text now lists for $217, and I find myself embarrassed to ask students at a public university to pay prices at this level. The main item on my wish list, other than the textbook being cheap and at the right level, is that it should be possible to teach the course by following the book closely for the entire year. (I have never taught the course before and have spent several years away from the universit
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Introduction: See more at the Statistics Forum (of course).
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Introduction: John Cook and Joseph Delaney point to an article by Yurii Aulchenko et al., who write: 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4-6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people. . . . In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. . . . The message is that the simple approach of predicting child’s height using a regression model given parents’ average height performs much better than the method they have based on combining 54 genes. They also find that, if you start with the prediction based on parents’ heigh
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Introduction: From Discover : Razib Khan asks: But follow the gradient from El Paso to the Illinois-Missouri border. The differences are small across state lines, but the consistent differences along the borders really don’t make. Are there state-level policies or regulations causing this? Or, are there state-level differences in measurement? This weird pattern shows up in other CDC data I’ve seen. Turns out that CDC isn’t providing data , they’re providing model . Frank Howland answered: I suspect the answer has to do with the manner in which the county estimates are produced. I went to the original data source, the CDC, and then to the relevant FAQ . There they say that the diabetes prevalence estimates come from the “CDC’s Behavioral Risk Factor Surveillance System (BRFSS) and data from the U.S. Census Bureau’s Population Estimates Program. The BRFSS is an ongoing, monthly, state-based telephone survey of the adult population. The survey provides state-specific informati
3 0.9605999 1013 andrew gelman stats-2011-11-16-My talk at Math for America on Saturday
Introduction: Here’s what I’ll talk about for 3 hours : Statistics—Inside and Outside the Classroom (1) Of Beauty, Sex, and Power: Statistical Challenges in the Estimation of Small Effects . A silly example of the frequencies of boy and girl babies leads us to some important research involving the meaning of statistical significance. (2) Mathematics, Statistics, and Political Science . We explore the differences between mathematical and statistical thinking, developing the ideas using examples from my own research in political science. (3) Statistics Teaching Activities . For twenty years I have been collecting class-participation demonstrations in statistics and probability. Here are some of my favorites.
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Introduction: The Organisation for Economic Co-operation and Development reports that the following project from Krisztina Szucs and Mate Cziner has won their visualization challenge, “launched in September 2012 to solicit visualisations based on the OECD’s data-rich Education at a Glance report”: (The graph is interactive. Click on the above image and click again to see the full version.) From the press release: Entries from around the world focused on data related to the economic costs and return on investment in education . . . [The winning entry] takes a detailed look at public vs. private and men vs. women for selected countries . . . The judges were particularly impressed by the angled slope format of the visualisation, which encourages comparison between the upper-secondary and tertiary benefits of education. Szucs and Cziner were also lauded for their striking visual design, which draws users into exploring their piece [emphasis added]. I used boldface to highlight a p
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Introduction: Joe Blitztein sent around the following graph: (The x-axis goes from 2000 to 2012 and the y=axis goes from 0 to 120.) 100 statistics majors (this combines sophomores, juniors, and seniors, but still, that’s a lot more than the 1 or 2 or 3 a year we’re used to seeing). At first I was like, whoa! But then I thought, why not 100 or even 200 or 300 statistics majors? Statistics is important in itself, it’s relatively easy as far as quantitative majors go, it’s applicable to lots of other areas. The real question should be not, What’s been happening that’s made statistics so trendy lately? but rather, What took so long for this to happen, and why isn’t statistics more popular? Both places where I studied as an undergraduate, statistics was just a subset of the math department, and maybe the only reason I ended up in statistics is that I took a probability course one semester because, at 5pm, it fit my schedule.
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