andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2347 knowledge-graph by maker-knowledge-mining
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Introduction: As I’ve written before, I was a math and physics major in college but I switched to statistics because math seemed pointless if you weren’t the best (and I knew there were people better than me), and I just didn’t feel like I had a good physical understanding. My lack of physical understanding comes up from time to time. An example occurred the other day. I was viewing a demonstration of Foucault’s pendulum and the guide said the period was something like 35 hours. I was surprised, having always thought it had a 24-hour period. Sure, I can understand it in words, but, even after reflecting on it for a minute, I couldn’t see my way to even an approximate derivation. If I wanted to, I think I could go through the math (for example here ), but I feel that if I really had the intuition, I wouldn’t need to. I’m sure I have a lot more physical understanding than the average person but I don’t think I have enough to be a real physicist.
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1 As I’ve written before, I was a math and physics major in college but I switched to statistics because math seemed pointless if you weren’t the best (and I knew there were people better than me), and I just didn’t feel like I had a good physical understanding. [sent-1, score-2.454]
2 My lack of physical understanding comes up from time to time. [sent-2, score-0.757]
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4 I was surprised, having always thought it had a 24-hour period. [sent-5, score-0.126]
5 Sure, I can understand it in words, but, even after reflecting on it for a minute, I couldn’t see my way to even an approximate derivation. [sent-6, score-0.533]
6 If I wanted to, I think I could go through the math (for example here ), but I feel that if I really had the intuition, I wouldn’t need to. [sent-7, score-0.871]
7 I’m sure I have a lot more physical understanding than the average person but I don’t think I have enough to be a real physicist. [sent-8, score-1.09]
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Introduction: As I’ve written before, I was a math and physics major in college but I switched to statistics because math seemed pointless if you weren’t the best (and I knew there were people better than me), and I just didn’t feel like I had a good physical understanding. My lack of physical understanding comes up from time to time. An example occurred the other day. I was viewing a demonstration of Foucault’s pendulum and the guide said the period was something like 35 hours. I was surprised, having always thought it had a 24-hour period. Sure, I can understand it in words, but, even after reflecting on it for a minute, I couldn’t see my way to even an approximate derivation. If I wanted to, I think I could go through the math (for example here ), but I feel that if I really had the intuition, I wouldn’t need to. I’m sure I have a lot more physical understanding than the average person but I don’t think I have enough to be a real physicist.
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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: Josh Tenenbaum describes some new work modeling people’s physical reasoning as probabilistic inferences over intuitive theories of mechanics. A general-purpose capacity for “physical intelligence”—inferring physical properties of objects and predicting future states in complex dynamical scenes—is central to how humans interpret their environment and plan safe and effective actions. The computations and representations underlying physical intelligence remain unclear, however. Cognitive studies have focused on mapping out judgment biases and errors, or on testing simple heuristic models suitable only for highly specific cases; they have not attempted to give general-purpose unifying models. In computer science, artificial intelligence and robotics researchers have long sought to formalize common-sense physical reasoning but without success in approaching human-level competence. Here we show that a wide range of human physical judgments can be explained by positing an “intuitive me
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Introduction: Some thoughts from Mark Palko: Of all the subjects a student is likely to encounter after elementary school, mathematics is by far the easiest to teach yourself. . . . What is it that makes math teachers so expendable? . . . At some point all disciplines require the transition from passive to active and that transition can be challenging. In courses like high school history and science, the emphasis on passively acquiring knowledge (yes, I realize that students write essays in history classes and apply formulas in science classes but that represents a relatively small portion of their time and, more importantly, the work those students do is fundamentally different from the day-to-day work done by historians and scientists). By comparison, junior high students playing in an orchestra, writing short stories or solving math problems are almost entirely focused on processes and those processes are essentially the same as those engaged in by professional musicians, writers and mat
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Introduction: As I’ve written before, I was a math and physics major in college but I switched to statistics because math seemed pointless if you weren’t the best (and I knew there were people better than me), and I just didn’t feel like I had a good physical understanding. My lack of physical understanding comes up from time to time. An example occurred the other day. I was viewing a demonstration of Foucault’s pendulum and the guide said the period was something like 35 hours. I was surprised, having always thought it had a 24-hour period. Sure, I can understand it in words, but, even after reflecting on it for a minute, I couldn’t see my way to even an approximate derivation. If I wanted to, I think I could go through the math (for example here ), but I feel that if I really had the intuition, I wouldn’t need to. I’m sure I have a lot more physical understanding than the average person but I don’t think I have enough to be a real physicist.
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Introduction: A few months ago, in response to some reflections from Steve Hsu, I wrote the following: I’ve long been glad that when I went to college in the 80s, I was at MIT rather than Harvard. Either place I would’ve taken hard classes and learned a lot, but one advantage of MIT was that we had no sense–no sense at all–that we could make big bucks. We had no sense of making moderately big bucks as lawyers, no sense of making big bucks working on Wall Street, and no sense of making really big bucks by starting a business. I mean, sure, we knew about lawyers (but we didn’t know that a lawyer with technical skills would be a killer combination), we knew about Wall Street (but we had no idea what they did, other than shout pork belly prices across a big room), and we knew about tech startups (but we had no idea that they were anything to us beyond a source of jobs for engineers). What we were all looking for was a good solid job with cool benefits (like those companies in California that had gy
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Introduction: Steve Hsu has posted a series of reflections here , here , and here on the dominance of graduates of HYPS (Harvard, Yale, Princeton, and Stanford (in that order, I believe)) in various Master-of-the-Universe-type jobs at “elite law firms, consultancies, and I-banks, hedge/venture funds, startups, and technology companies.” Hsu writes: In the real world, people believe in folk notions of brainpower or IQ. (“Quick on the uptake”, “Picks things up really fast”, “A sponge” …) They count on elite educational institutions to do their g-filtering for them. . . . Most top firms only recruit at a few schools. A kid from a non-elite UG school has very little chance of finding a job at one of these places unless they first go to grad school at, e.g., HBS, HLS, or get a PhD from a top place. (By top place I don’t mean “gee US News says Ohio State’s Aero E program is top 5!” — I mean, e.g., a math PhD from Berkeley or a PhD in computer science from MIT — the traditional top dogs in academ
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Introduction: As I’ve written before, I was a math and physics major in college but I switched to statistics because math seemed pointless if you weren’t the best (and I knew there were people better than me), and I just didn’t feel like I had a good physical understanding. My lack of physical understanding comes up from time to time. An example occurred the other day. I was viewing a demonstration of Foucault’s pendulum and the guide said the period was something like 35 hours. I was surprised, having always thought it had a 24-hour period. Sure, I can understand it in words, but, even after reflecting on it for a minute, I couldn’t see my way to even an approximate derivation. If I wanted to, I think I could go through the math (for example here ), but I feel that if I really had the intuition, I wouldn’t need to. I’m sure I have a lot more physical understanding than the average person but I don’t think I have enough to be a real physicist.
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Introduction: Somebody named David writes: I [David] thought you might be interested or have an opinion on the paper referenced below. I am largely skeptical on the techniques presented and thought you might have some insight because you work with datasets more similar to those in ‘social science’ than myself. Dana and Dawes. The superiority of simple alternatives to regression for social science predictions. Journal of Educational and Behavioral Statistics (2004) vol. 29 (3) pp. 317. My reply: I read the abstract (available online) and it seemed reasonable to me. They prefer simple averages or weights based on correlations rather than regressions. From a Bayesian perspective, what they’re saying is that least-squares regression and similar methods are noisy, and they can do better via massive simplification. I’ve been a big fan of Robyn Dawes ever since reading his article in the classic Kahneman, Slovic, and Tversky volume. I have no idea how much Dawes knows about modern Bayesian
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Introduction: In a discussion of his variant of the write-a-thousand-words-a-day strategy (as he puts it, “a system for the production of academic results in writing”), Thomas Basbøll writes : Believe the claims you are making. That is, confine yourself to making claims you believe. I always emphasize this when I [Basbøll] define knowledge as “justified, true belief”. . . . I think if there is one sure way to undermine your sense of your own genius it is to begin to say things you know to be publishable without being sure they are true. Or even things you know to be “true” but don’t understand well enough to believe. He points out that this is not so easy: In times when there are strong orthodoxies it can sometimes be difficult to know what to believe. Or, rather, it is all too easy to know what to believe (what the “right belief” is). It is therefore difficult to stick to statements of one’s own belief. I sometimes worry that our universities, which are systems of formal education and for
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