andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2339 knowledge-graph by maker-knowledge-mining
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Introduction: Mon: My short career as a Freud expert Tues: “P.S. Is anyone working on hierarchical survival models?” Wed: Skepticism about a published claim regarding income inequality and happiness Thurs: Big Data needs Big Model Fri: Did Neyman really say of Fisher’s work, “It’s easy to get the right answer if you never define what the question is,” and did Fisher really describe Neyman as “a theorem-proving poseur who wouldn’t recognized real data if it bit him in the ass” Sat: An interesting mosaic of a data programming course Sun: Why I decided not to be a physicist
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same-blog 1 1.0 2339 andrew gelman stats-2014-05-19-On deck this week
Introduction: Mon: My short career as a Freud expert Tues: “P.S. Is anyone working on hierarchical survival models?” Wed: Skepticism about a published claim regarding income inequality and happiness Thurs: Big Data needs Big Model Fri: Did Neyman really say of Fisher’s work, “It’s easy to get the right answer if you never define what the question is,” and did Fisher really describe Neyman as “a theorem-proving poseur who wouldn’t recognized real data if it bit him in the ass” Sat: An interesting mosaic of a data programming course Sun: Why I decided not to be a physicist
2 0.4966096 2320 andrew gelman stats-2014-05-05-On deck this month
Introduction: Can we make better graphs of global temperature history? Priors I don’t believe Cause he thinks he’s so-phisticated Discussion with Steven Pinker on research that is attached to data that are so noisy as to be essentially uninformative Combining forecasts: Evidence on the relative accuracy of the simple average and Bayesian model averaging for predicting social science problems What property is important in a risk prediction model? Discrimination or calibration? “What should you talk about?” Science tells us that fast food lovers are more likely to marry other fast food lovers Personally, I’d rather go with Teragram How much can we learn about individual-level causal claims from state-level correlations? Bill Easterly vs. Jeff Sachs: What percentage of the recipients didn’t use the free malaria bed nets in Zambia? Models with constraints Forum in Ecology on p-values and model selection Never back down: The culture of poverty and the culture of journalism M
3 0.33849299 1869 andrew gelman stats-2013-05-24-In which I side with Neyman over Fisher
Introduction: As a data analyst and a scientist, Fisher > Neyman, no question. But as a theorist, Fisher came up with ideas that worked just fine in his applications but can fall apart when people try to apply them too generally. Here’s an example that recently came up. Deborah Mayo pointed me to a comment by Stephen Senn on the so-called Fisher and Neyman null hypotheses. In an experiment with n participants (or, as we used to say, subjects or experimental units), the Fisher null hypothesis is that the treatment effect is exactly 0 for every one of the n units, while the Neyman null hypothesis is that the individual treatment effects can be negative or positive but have an average of zero. Senn explains why Neyman’s hypothesis in general makes no sense—the short story is that Fisher’s hypothesis seems relevant in some problems (sometimes we really are studying effects that are zero or close enough for all practical purposes), whereas Neyman’s hypothesis just seems weird (it’s implausible
4 0.23874849 2240 andrew gelman stats-2014-03-10-On deck this week: Things people sent me
Introduction: Mon: Preregistration: what’s in it for you? Tues: What if I were to stop publishing in journals? Wed: Empirical implications of Empirical Implications of Theoretical Models Thurs: An Economist’s Guide to Visualizing Data Fri: The maximal information coefficient Sat: Problematic interpretations of confidence intervals Sun: The more you look, the more you find
5 0.23541608 2290 andrew gelman stats-2014-04-14-On deck this week
Introduction: Mon : Transitioning to Stan Tues : When you believe in things that you don’t understand Wed : Looking for Bayesian expertise in India, for the purpose of analysis of sarcoma trials Thurs : If you get to the point of asking, just do it. But some difficulties do arise . . . Fri : One-tailed or two-tailed? Sat : Index or indicator variables Sun : Fooled by randomness
6 0.22937116 2366 andrew gelman stats-2014-06-09-On deck this week
7 0.22651333 2348 andrew gelman stats-2014-05-26-On deck this week
8 0.22016451 1880 andrew gelman stats-2013-06-02-Flame bait
9 0.21292949 2321 andrew gelman stats-2014-05-05-On deck this week
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11 0.20005046 2331 andrew gelman stats-2014-05-12-On deck this week
12 0.19859193 2310 andrew gelman stats-2014-04-28-On deck this week
13 0.18303639 2253 andrew gelman stats-2014-03-17-On deck this week: Revisitings
14 0.17621845 2222 andrew gelman stats-2014-02-24-On deck this week
15 0.17454211 2206 andrew gelman stats-2014-02-10-On deck this week
16 0.17378135 2298 andrew gelman stats-2014-04-21-On deck this week
17 0.16787297 2356 andrew gelman stats-2014-06-02-On deck this week
18 0.16457728 2285 andrew gelman stats-2014-04-07-On deck this week
19 0.1531968 2265 andrew gelman stats-2014-03-24-On deck this week
20 0.15112737 2338 andrew gelman stats-2014-05-19-My short career as a Freud expert
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same-blog 1 0.93042237 2339 andrew gelman stats-2014-05-19-On deck this week
Introduction: Mon: My short career as a Freud expert Tues: “P.S. Is anyone working on hierarchical survival models?” Wed: Skepticism about a published claim regarding income inequality and happiness Thurs: Big Data needs Big Model Fri: Did Neyman really say of Fisher’s work, “It’s easy to get the right answer if you never define what the question is,” and did Fisher really describe Neyman as “a theorem-proving poseur who wouldn’t recognized real data if it bit him in the ass” Sat: An interesting mosaic of a data programming course Sun: Why I decided not to be a physicist
2 0.88100892 2240 andrew gelman stats-2014-03-10-On deck this week: Things people sent me
Introduction: Mon: Preregistration: what’s in it for you? Tues: What if I were to stop publishing in journals? Wed: Empirical implications of Empirical Implications of Theoretical Models Thurs: An Economist’s Guide to Visualizing Data Fri: The maximal information coefficient Sat: Problematic interpretations of confidence intervals Sun: The more you look, the more you find
3 0.84027082 2331 andrew gelman stats-2014-05-12-On deck this week
Introduction: Mon: “The results (not shown) . . .” Tues: Personally, I’d rather go with Teragram Wed: How much can we learn about individual-level causal claims from state-level correlations? Thurs: Bill Easterly vs. Jeff Sachs: What percentage of the recipients didn’t use the free malaria bed nets in Zambia? Fri: Models with constraints Sat: Forum in Ecology on p-values and model selection Sun: Never back down: The culture of poverty and the culture of journalism
4 0.83924234 2366 andrew gelman stats-2014-06-09-On deck this week
Introduction: Mon: I hate polynomials Tues: Spring forward, fall back, drop dead? Wed: Bayes in the research conversation Thurs: The health policy innovation center: how best to move from pilot studies to large-scale practice? Fri: Stroopy names Sat: He’s not so great in math but wants to do statistics and machine learning Sun: Comparing the full model to the partial model
5 0.82641816 2298 andrew gelman stats-2014-04-21-On deck this week
Introduction: Mon : Ticket to Baaaath Tues : Ticket to Baaaaarf Wed : Thinking of doing a list experiment? Here’s a list of reasons why you should think again Thurs : An open site for researchers to post and share papers Fri : Questions about “Too Good to Be True” Sat : Sleazy sock puppet can’t stop spamming our discussion of compressed sensing and promoting the work of Xiteng Liu Sun : White stripes and dead armadillos
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20 0.63074726 2264 andrew gelman stats-2014-03-24-On deck this month
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same-blog 1 0.95167124 2339 andrew gelman stats-2014-05-19-On deck this week
Introduction: Mon: My short career as a Freud expert Tues: “P.S. Is anyone working on hierarchical survival models?” Wed: Skepticism about a published claim regarding income inequality and happiness Thurs: Big Data needs Big Model Fri: Did Neyman really say of Fisher’s work, “It’s easy to get the right answer if you never define what the question is,” and did Fisher really describe Neyman as “a theorem-proving poseur who wouldn’t recognized real data if it bit him in the ass” Sat: An interesting mosaic of a data programming course Sun: Why I decided not to be a physicist
2 0.88020694 1982 andrew gelman stats-2013-08-15-Blaming scientific fraud on the Kuhnians
Introduction: I wouldn’t go that far, but I’ll send along this article by Ahti-Veikko Pietarinen that was sent to me by Lee Sechrest. Those of you who like this sort of thing might like this sort of thing. I neither endorse nor anti-endorse. Or, I should say, I am in sympathy with the author’s general attitude but I have not looked at any of the details.
3 0.87592703 708 andrew gelman stats-2011-05-12-Improvement of 5 MPG: how many more auto deaths?
Introduction: This entry was posted by Phil Price. A colleague is looking at data on car (and SUV and light truck) collisions and casualties. He’s interested in causal relationships. For instance, suppose car manufacturers try to improve gas mileage without decreasing acceleration. The most likely way they will do that is to make cars lighter. But perhaps lighter cars are more dangerous; how many more people will die for each mpg increase in gas mileage? There are a few different data sources, all of them seriously deficient from the standpoint of answering this question. Deaths are very well reported, so if someone dies in an auto accident you can find out what kind of car they were in, what other kinds of cars (if any) were involved in the accident, whether the person was a driver or passenger, and so on. But it’s hard to normalize: OK, I know that N people who were passengers in a particular model of car died in car accidents last year, but I don’t know how many passenger-miles that
4 0.8739031 173 andrew gelman stats-2010-07-31-Editing and clutch hitting
Introduction: Regarding editing : The only serious editing I’ve ever received has been for my New York Times op-eds and my article in the American Scientist. My book editors have all been nice people, and they’ve helped me with many things (including suggestions of what my priorities should be in communicating with readers)–they’ve been great–but they’ve not given (nor have I expected or asked for) serious editing. Maybe I should’ve asked for it, I don’t know. I’ve had time-wasting experiences with copy editors and a particularly annoying experience with a production editor (who was so difficult that my coauthors and I actually contacted our agent and a lawyer about the possibility of getting out of our contract), but that’s another story. Regarding clutch hitting , Bill James once noted that it’s great when a Bucky Dent hits an unexpected home run, but what’s really special is being able to get the big hit when it’s expected of you. The best players can do their best every time they come t
5 0.87358618 2079 andrew gelman stats-2013-10-27-Uncompressing the concept of compressed sensing
Introduction: I received the following email: These compressed sensing people link to Shannon’s advice . It’s refreshing when leaders of a field state that their stuff may not be a panacea. I replied: Scarily enough, I don’t know anything about this research area at all! My correspondent followed up: Meh. They proved L1 approximates L0 when design matrix is basically full rank. Now all sparsity stuff is sometimes called ‘compressed sensing’. Most of it seems to be linear interpolation, rebranded. I wrote back: But rebranding/reframing can be useful! Often reframing is a step in the direction of improvement, of better understanding one’s assumptions and goals.
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