andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1745 knowledge-graph by maker-knowledge-mining
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Introduction: 15-2040 != 19-3010 (and, for that matter, 25-1022 != 25-1063).
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same-blog 1 1.0 1745 andrew gelman stats-2013-03-02-Classification error
Introduction: 15-2040 != 19-3010 (and, for that matter, 25-1022 != 25-1063).
2 0.15217504 691 andrew gelman stats-2011-05-03-Psychology researchers discuss ESP
Introduction: Chris Masse writes: I know you hate the topic, but during this debate (discussing both sides), they were issues raised that are of interest of your science. Actually I just don’t have the patience to watch videos. But I’ll forward it on to the rest of you. I’ve already posted my thoughts on the matter here . ESP is certainly something that a lot of people want to be true.
3 0.14558588 83 andrew gelman stats-2010-06-13-Silly Sas lays out old-fashioned statistical thinking
Introduction: People keep telling me that Sas isn’t as bad as everybody says, but then I see (from Christian Robert ) this listing from the Sas website of “disadvantages in using Bayesian analysis”: There is no correct way to choose a prior. Bayesian inferences require skills to translate prior beliefs into a mathematically formulated prior. If you do not proceed with caution, you can generate misleading results. . . . From a practical point of view, it might sometimes be difficult to convince subject matter experts who do not agree with the validity of the chosen prior. That is so tacky! As if least squares, logistic regressions, Cox models, and all those other likelihoods mentioned in the Sas documentation are so automatically convincing to subject matter experts. P.S. For some more serious objections to Bayesian statistics, see here and here . P.P.S. In case you’re wondering why I’m commenting on month-old blog entries . . . I have a monthlong backlog of entries, and I’m spooling
4 0.11885627 583 andrew gelman stats-2011-02-21-An interesting assignment for statistical graphics
Introduction: Antony Unwin writes: I [Unwin] find it an interesting exercise for students to ask them to write headlines (and subheadlines) for graphics, both for ones they have drawn themselves and for published ones. The results are sometimes depressing, often thought-provoking and occasionally highly entertaining. This seems like a great idea, both for teaching students how to read a graph and also for teaching how to make a graph. I’ve long said that when making a graph (or, for that matter, a table), you want to think about what message the reader will get out of it. “Displaying a bunch of numbers” doesn’t cut it.
5 0.11243706 2329 andrew gelman stats-2014-05-11-“What should you talk about?”
Introduction: Tyler Cowen quotes Robin Hanson: If your main reason for talking is to socialize, you’ll want to talk about whatever everyone else is talking about. Like say the missing Malaysia Airlines plane. But if instead your purpose is to gain and spread useful insight, so that we can all understand more about things that matter, you’ll want to look for relatively neglected topics. . . . One advantage of having this blog on a lag of a month or two is that I can post things, knowing that when my discussion finally appears, it will no longer be topical. Indeed, this post is an example.
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18 0.073328622 244 andrew gelman stats-2010-08-30-Useful models, model checking, and external validation: a mini-discussion
19 0.070421681 184 andrew gelman stats-2010-08-04-That half-Cauchy prior
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same-blog 1 0.99999994 1745 andrew gelman stats-2013-03-02-Classification error
Introduction: 15-2040 != 19-3010 (and, for that matter, 25-1022 != 25-1063).
2 0.4458341 747 andrew gelman stats-2011-06-06-Research Directions for Machine Learning and Algorithms
Introduction: After reading this from John Langford: The Deep Learning problem remains interesting. How do you effectively learn complex nonlinearities capable of better performance than a basic linear predictor? An effective solution avoids feature engineering. Right now, this is almost entirely dealt with empirically, but theory could easily have a role to play in phrasing appropriate optimization algorithms, for example. Jimmy asks: Does this sound related to modeling the deep interactions you often talk about? (I [Jimmy] never understand the stuff on hunch, but thought that might be so?) My reply: I don’t understand that stuff on hunch so well either–he uses slightly different jargon than I do! That said, it looks interesting and important so I’m pointing you all to it.
3 0.44545373 1785 andrew gelman stats-2013-04-02-So much artistic talent
Introduction: I saw this excellent art show the other day, and it reminded me how much artistic talent is out there. I really have no idea whassup with those all-black canvases and the other stuff you see at modern art museums, given that there’s so much interesting new stuff being created every year. I see a big difference between art made by people who feel they have something they want to say, compared to art being made by people who feel they are supposed to make art because they’re artists. And there’s also the internal logic of art responding to other art, as Tom Wolfe discussed in The Painted Word.
4 0.44138062 997 andrew gelman stats-2011-11-07-My contribution to the discussion on “Should voting be mandatory?”
Introduction: My short answer (based on the research of Leighley and Nagler): Whether or not mandatory voting is a good idea, I think it’s unlikely to happen at a national level. Even setting aside the practical difficulties of taking a now-voluntary action and making it compulsory, bringing in the other half of the potential electorate would change the political conversation so much that it’s hard for me to see current officeholders supporting such a plan. For the full story, see here .
5 0.43514895 2001 andrew gelman stats-2013-08-29-Edgar Allan Poe was a statistician
Introduction: Antony Unwin writes: Rereading Edgar Allan Poe’s “Murder in the Rue Morgue” reminded me of his astute remarks on analysis. For instance But it is in matters beyond the limits of mere rule that the skill of the analyst is evinced. He makes, in silence, a host of observations and inferences. and and the difference in the extent of the information obtained, lies not so much in the validity of the inference as in the quality of the observation. The necessary knowledge is that of what to observe. and He impaired his vision by holding the object too close. He might see, perhaps, one or two points with unusual clearness, but in so doing he, necessarily, lost sight of the matter as a whole. However, I had forgotten his following comment, which rang all sorts of bells in connection with some scientific articles I have seen recently: what is only complex is mistaken (a not unusual error) for what is profound. How about asking referees to rate articles on their complex
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same-blog 1 1.0 1745 andrew gelman stats-2013-03-02-Classification error
Introduction: 15-2040 != 19-3010 (and, for that matter, 25-1022 != 25-1063).
2 0.99004561 1026 andrew gelman stats-2011-11-25-Bayes wikipedia update
Introduction: I checked and somebody went in and screwed up my fixes to the wikipedia page on Bayesian inference. I give up.
3 0.91511363 572 andrew gelman stats-2011-02-14-Desecration of valuable real estate
Introduction: Malecki asks: Is this the worst infographic ever to appear in NYT? USA Today is not something to aspire to. To connect to some of our recent themes , I agree this is a pretty horrible data display. But it’s not bad as a series of images. Considering the competition to be a cartoon or series of photos, these images aren’t so bad. One issue, I think, is that designers get credit for creativity and originality (unusual color combinations! Histogram bars shaped like mosques!) , which is often the opposite of what we want in a clear graph. It’s Martin Amis vs. George Orwell all over again.
4 0.9089241 1014 andrew gelman stats-2011-11-16-Visualizations of NYPD stop-and-frisk data
Introduction: Cathy O’Neil organized this visualization project with NYPD stop-and-frisk data. It’s part of the Data Without Borders project. Unfortunately, because of legal restrictions I couldn’t send them the data Jeff, Alex, and I used in our project several years ago.
5 0.90369081 398 andrew gelman stats-2010-11-06-Quote of the day
Introduction: “A statistical model is usually taken to be summarized by a likelihood, or a likelihood and a prior distribution, but we go an extra step by noting that the parameters of a model are typically batched, and we take this batching as an essential part of the model.”
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