andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-447 knowledge-graph by maker-knowledge-mining
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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
sentIndex sentText sentNum sentScore
1 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. [sent-1, score-0.594]
2 The total sum of these individual areas thus represents the total area under the curve. [sent-3, score-1.191]
3 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. [sent-4, score-1.319]
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. [sent-6, score-1.608]
5 You know, that thing most readers of this blog were taught back in 11th or 12th grade, and all med students were taught by freshman year in college. [sent-7, score-0.689]
6 The blogger finds this amusing, but I find it mostly upsetting and sad. [sent-8, score-0.334]
7 Which is sadder: (1) That this paper got past the referees, (2) that it has been cited dozens of times in the medical literature, including this year, (3) that, if the abstract is to be believed, many medical researchers DON’T use an accurate method to calculate the area under a curve. [sent-9, score-1.226]
8 I, too, have published results that I’ve later found were previously published by someone else. [sent-11, score-0.074]
9 But I’ve never done it with something that is taught in high school calculus. [sent-12, score-0.2]
10 And — I’m practically spluttering with indignation — if I wanted to calculate something like the area under a curve, I would at least first see if there is already a known way to do it! [sent-13, score-0.773]
11 I wouldn’t invent an obvious method, name it after myself, and send it to a journal, without it ever occurring to me that, gee, maybe someone else has thought about this already! [sent-14, score-0.287]
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same-blog 1 1.0000002 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: As we get more data, we can fit more model. But at some point we become so overwhelmed by data that, for computational reasons, we can barely do anything at all. Thus, the curve above could be thought of as the product of two curves: a steadily increasing curve showing the statistical ability to fit more complex models with more data, and a steadily decreasing curve showing the computational feasibility of doing so.
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Introduction: After reading all the comments here I remembered that I’ve actually written a paper on the generalized method of moments–including the bit about maximum likelihood being a special case. The basic idea is simple enough that it must have been rediscovered dozens of times by different people (sort of like the trapezoidal rule ). In our case, we were motivated to (independently) develop the (well-known, but not by me) generalized method of moments as a way of specifying an indirectly-parameterized prior distribution, rather than as a way of estimating parameters from direct data. But the math is the same.
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Introduction: Matthew Yglesias links approvingly to the following statement by Michael Mandel: Homeland Security accounts for roughly 90% of the increase in federal regulatory employment over the past ten years. Roughly 90%, huh? That sounds pretty impressive. But wait a minute . . . what if total federal regulatory employment had increased a bit less. Then Homeland Security could’ve accounted for 105% of the increase, or 500% of the increase, or whatever. The point is the change in total employment is the sum of a bunch of pluses and minuses. It happens that, if you don’t count Homeland Security, the total hasn’t changed much–I’m assuming Mandel’s numbers are correct here–and that could be interesting. The “roughly 90%” figure is misleading because, when written as a percent of the total increase, it’s natural to quickly envision it as a percentage that is bounded by 100%. There is a total increase in regulatory employment that the individual agencies sum to, but some margins are p
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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: In a rare Christmas-themed post here, I pass along this note from Alexander Berger at GiveWell : We just published a blog post following up on the *other* famous piece of evidence for deworming, the Miguel and Kremer experiment from Kenya. They shared data and code from their working paper (!) follow-up finding that deworming increases incomes ten years later, and we came out of the re-analysis feeling more confident in, though not wholly convinced by, the results. We’ve also just released our new list of top charities for giving season this year, which I think might be a good fit for your audience. We wrote a blog post explaining our choices , and have also published extensive reviews of the top charities and the interventions on which they work. Perhaps the most interesting change since last year is the addition of GiveDirectly in the #2 spot; they do direct unconditional cash transfers to people living on less than a dollar a day in Kenya. We think it’s a remarkable mode
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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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Introduction: Blogger Deep Climate looks at another paper by the 2002 recipient of the American Statistical Association’s Founders award. This time it’s not funny, it’s just sad. Here’s Wikipedia on simulated annealing: By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random “nearby” solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly “downhill” as T goes to zero. The allowance for “uphill” moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. And here’s Wegman: During each step of the algorithm, the variable that will eventually represent the minimum is replaced by a random solution that is chosen according to a temperature
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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
Introduction: Wayne Folta points me to “EigenBracket 2012: Using Graph Theory to Predict NCAA March Madness Basketball” and writes, “I [Folta] have got to believe that he’s simply re-invented a statistical method in a graph-ish context, but don’t know enough to judge.” I have not looked in detail at the method being presented here—I’m not much of college basketball fan—but I’d like to use this as an excuse to make one of my favorite general point, which is that a good way to characterize any statistical method is by what information it uses. The basketball ranking method here uses score differentials between teams in the past season. On the plus side, that is better than simply using one-loss records (which (a) discards score differentials and (b) discards information on who played whom). On the minus side, the method appears to be discretizing the scores (thus throwing away information on the exact score differential) and doesn’t use any external information such as external ratings. A
3 0.95865655 1626 andrew gelman stats-2012-12-16-The lamest, grudgingest, non-retraction retraction ever
Introduction: In politics we’re familiar with the non-apology apology (well described in Wikipedia as “a statement that has the form of an apology but does not express the expected contrition”). Here’s the scientific equivalent: the non-retraction retraction. Sanjay Srivastava points to an amusing yet barfable story of a pair of researchers who (inadvertently, I assume) made a data coding error and were eventually moved to issue a correction notice, but even then refused to fully admit their error. As Srivastava puts it, the story “ended up with Lew [Goldberg] and colleagues [Kibeom Lee and Michael Ashton] publishing a comment on an erratum – the only time I’ve ever heard of that happening in a scientific journal.” From the comment on the erratum: In their “erratum and addendum,” Anderson and Ones (this issue) explained that we had brought their attention to the “potential” of a “possible” misalignment and described the results computed from re-aligned data as being based on a “post-ho
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