andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-976 knowledge-graph by maker-knowledge-mining
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Introduction: Continuing “heckle the press” month here at the blog, I (Bob) found the following “discovery” a little overplayed by David H. Freedman , who was writing for Scientific American in the following article and blog post: Blog: Why Economic Models are Always Wrong Article: A Formula for Economic Calamity The article’s paywalled, but the blog entry isn’t. Apparently, a geophysicist named Jonathan Carter (good luck finding him on the web given only that information) found that when he simulated from a complicated model, then fit the model to the simulated data, he sometimes got different results. What’s more, these differing estimates fit the data equally well but made different predictions on new data. Now we don’t know if the model was identifiable, had different local optima (i.e., multiple modes), how he fit the data, or really anything, but it doesn’t really matter. Reading the comments and article is a depressing exercise in the sociology of science, with clueles
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1 Freedman , who was writing for Scientific American in the following article and blog post: Blog: Why Economic Models are Always Wrong Article: A Formula for Economic Calamity The article’s paywalled, but the blog entry isn’t. [sent-2, score-0.335]
2 Apparently, a geophysicist named Jonathan Carter (good luck finding him on the web given only that information) found that when he simulated from a complicated model, then fit the model to the simulated data, he sometimes got different results. [sent-3, score-0.608]
3 What’s more, these differing estimates fit the data equally well but made different predictions on new data. [sent-4, score-0.212]
4 , multiple modes), how he fit the data, or really anything, but it doesn’t really matter. [sent-7, score-0.11]
5 So I’m going to join the crowd and file it under “the press misunderstanding science”. [sent-9, score-0.207]
6 Cluefulness to the Rescue I’ll maintain my optimism about humanity as a whole, though, because some of the commenters were on the right track. [sent-10, score-0.313]
7 Commenter number six, jayjacobugs , gets it right, pointing out that scientists should be doing sensitivity analyses (though I’d look at more than just “changing variables”). [sent-11, score-0.086]
8 And then commenter thirteen, LeighCaldwell , reminds the readers that economists are well aware of this problem. [sent-12, score-0.204]
9 At the risk of sounding like an old curmudgeon, I remember when Scientific American enlisted domain experts to write articles. [sent-14, score-0.282]
10 I was envious that my boss at Bell Labs, Steve Levinson (along with Mark Liberman, of The Language Log ), had written a Scientific American article on speech recognition. [sent-15, score-0.418]
11 The article didn’t assume calculus, but it also didn’t assume readers were idiots waiting to be titillated by a “scandal. [sent-16, score-0.376]
12 ” One of the things that got me interested in math and science was the mathematical games column of Martin Gardner , from which you could learn serious mathematical reasoning with only American middle-school math classes. [sent-17, score-0.736]
13 Speaking of American math education, kids in the U. [sent-18, score-0.224]
14 who like math should envy Christian Robert ‘s daughter, who is learning Monte Carlo integration in the equivalent of American tenth grade. [sent-20, score-0.424]
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same-blog 1 0.99999976 976 andrew gelman stats-2011-10-27-Geophysicist Discovers Modeling Error (in Economics)
Introduction: Continuing “heckle the press” month here at the blog, I (Bob) found the following “discovery” a little overplayed by David H. Freedman , who was writing for Scientific American in the following article and blog post: Blog: Why Economic Models are Always Wrong Article: A Formula for Economic Calamity The article’s paywalled, but the blog entry isn’t. Apparently, a geophysicist named Jonathan Carter (good luck finding him on the web given only that information) found that when he simulated from a complicated model, then fit the model to the simulated data, he sometimes got different results. What’s more, these differing estimates fit the data equally well but made different predictions on new data. Now we don’t know if the model was identifiable, had different local optima (i.e., multiple modes), how he fit the data, or really anything, but it doesn’t really matter. Reading the comments and article is a depressing exercise in the sociology of science, with clueles
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Introduction: My column in Scientific American . Check out the comments. I have to remember never ever to write about guns.
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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
Introduction: Steven Hayward at the American Enterprise Institute wrote an article , sure to attract the attention of people such as myself, entitled, “The irrelevance of modern political science,” in which he discusses some silly-sounding papers presented at the recent American Political Science Association and then moves to a larger critique of quantitative political science: I [Hayward] have often taken a random article from the American Political Science Review, which resembles a mathematical journal on most of its pages, and asked students if they can envision this method providing the mathematical formula that will deliver peace in the Middle East. Even the dullest students usually grasp the point without difficulty. At the sister blog, John Sides discusses and dismisses Hayward’s arguments, point on that, among other things, political science might very well be useful even if it doesn’t deliver peace in the Middle East. After all, the U.S. Army didn’t deliver peace in the Midd
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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: Continuing “heckle the press” month here at the blog, I (Bob) found the following “discovery” a little overplayed by David H. Freedman , who was writing for Scientific American in the following article and blog post: Blog: Why Economic Models are Always Wrong Article: A Formula for Economic Calamity The article’s paywalled, but the blog entry isn’t. Apparently, a geophysicist named Jonathan Carter (good luck finding him on the web given only that information) found that when he simulated from a complicated model, then fit the model to the simulated data, he sometimes got different results. What’s more, these differing estimates fit the data equally well but made different predictions on new data. Now we don’t know if the model was identifiable, had different local optima (i.e., multiple modes), how he fit the data, or really anything, but it doesn’t really matter. Reading the comments and article is a depressing exercise in the sociology of science, with clueles
2 0.75945133 447 andrew gelman stats-2010-12-03-Reinventing the wheel, only more so.
Introduction: Posted by Phil Price: A blogger (can’t find his name anywhere on his blog) points to an article in the medical literature in 1994 that is…well, it’s shocking, is what it is. This is from the abstract: In Tai’s Model, the total area under a curve is computed by dividing the area under the curve between two designated values on the X-axis (abscissas) into small segments (rectangles and triangles) whose areas can be accurately calculated from their respective geometrical formulas. The total sum of these individual areas thus represents the total area under the curve. Validity of the model is established by comparing total areas obtained from this model to these same areas obtained from graphic method (less than +/- 0.4%). Other formulas widely applied by researchers under- or overestimated total area under a metabolic curve by a great margin Yes, that’s right, this guy has rediscovered the trapezoidal rule. You know, that thing most readers of this blog were taught back in 1
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Introduction: Rajiv Sethi quotes Bentley University economics professor Scott Sumner writing on the first anniversary of his blog: Be careful what you wish for. Last February 2nd I [Sumner] started this blog with very low expectations… I knew I wasn’t a good writer . . . And I was also pretty sure that the content was not of much interest to anyone. Now my biggest problem is time–I spend 6 to 10 hours a day on the blog, seven days a week. Several hours are spent responding to reader comments and the rest is spent writing long-winded posts and checking other economics blogs. . . . I [Sumner] don’t think much of the official methodology in macroeconomics. Many of my fellow economists seem to have a Popperian view of the social sciences. You develop a model. You go out and get some data. And then you try to refute the model with some sort of regression analysis. . . . My problem with this view is that it doesn’t reflect the way macro and finance actually work. Instead the models are
Introduction: My friend Seth, whom I know from Berkeley (we taught a course together on left-handedness), has a blog on topics ranging from thoughtful discussions of scientific evidence, to experiences with his unconventional weight-loss scheme, offbeat self-experimentation, and advocacy of fringe scientific theories, leavened with occasional dollops of cynicism and political extremism . I agree with Seth on some things but not others. ( Here’s Seth’s reason for not attempting a clinical trial of his diet.) Recently I was disturbed (but, I’m sorry to say, not surprised) to see Seth post the following: Predictions of climate models versus reality . I [Seth] have only seen careful prediction-vs-reality comparisons made by AGW [anthropogenic global warming] skeptics. Those who believe humans are dangerously warming the planet appear to be silent on this subject. In response, Phil commented : Funny, on the day you [Seth] made your post saying that you haven’t seen comparis
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Introduction: Commenter Zbicyclist links to a fun article by Howard French on biologist E. O. Wilson: Wilson announced that his new book may be his last. It is not limited to the discussion of evolutionary biology, but ranges provocatively through the humanities, as well. . . . Generation after generation of students have suffered trying to “puzzle out” what great thinkers like Socrates, Plato, and Descartes had to say on the great questions of man’s nature, Wilson said, but this was of little use, because philosophy has been based on “failed models of the brain.” This reminds me of my recent remarks on the use of crude folk-psychology models as microfoundations for social sciences. The article also discusses Wilson’s recent crusade against selfish-gene-style simplifications of human and animal nature. I’m with Wilson 100% on this one. “Two brothers or eight cousins” is a cute line but it doesn’t seem to come close to describing how species or societies work, and it’s always seemed a
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Introduction: Mark Palko defines a Ddulite as follows: A preference for higher tech solutions even in cases where lower tech alternatives have greater and more appropriate functionality; a person of ddulite tendencies. Though Ddulites are the opposite of Luddites with respect to attitudes toward technology, they occupy more or less the same point with respect to functionality. As a sometime Luddite myself (no cell phone, tv, microwave oven, etc.), I should in fairness point out the logic in favor of being a Ddulite. Old technology is typically pretty stable; new technology is improving. It can make sense to switch early (before the new technology actually performs better than the old) to get the benefits of being familiar with the new technology once it does take off.
Introduction: That’s the title of my talk at Montana State University this Thursday (21 Mar). For those of you who happen to be in the area, it’s 3:30-5:00pm in the Procrastinator Theater. I’m also speaking in the statistics seminar from 11-12:15 in the Byker Auditorium in the Chemistry building. Topic: Causality and Statistical Learning . P.S. My title is a bit Geng-inspired, which reminds me that I recently came across this interesting mini-bio.
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Introduction: Martin Lindquist writes that he and others are trying to start a new ASA section on statistics in imaging. If you’re interested in being a signatory to its formation, please send him an email.
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Introduction: Alex Braunstein writes: Thanks for the post . You drove >800 pageviews to my site. That’s >90% of what Robert Scoble’s tweet generated with 184k followers, which I find incredibly impressive. 800 doesn’t sound like so much to me, but I suppose if it’s the right 800 . . .
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Introduction: Continuing “heckle the press” month here at the blog, I (Bob) found the following “discovery” a little overplayed by David H. Freedman , who was writing for Scientific American in the following article and blog post: Blog: Why Economic Models are Always Wrong Article: A Formula for Economic Calamity The article’s paywalled, but the blog entry isn’t. Apparently, a geophysicist named Jonathan Carter (good luck finding him on the web given only that information) found that when he simulated from a complicated model, then fit the model to the simulated data, he sometimes got different results. What’s more, these differing estimates fit the data equally well but made different predictions on new data. Now we don’t know if the model was identifiable, had different local optima (i.e., multiple modes), how he fit the data, or really anything, but it doesn’t really matter. Reading the comments and article is a depressing exercise in the sociology of science, with clueles
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