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1 andrew gelman stats-2012-12-31-Statistical modeling, causal inference, and social science

Introduction: Interesting discussion by Berk Ozler (which I found following links from Tyler Cowen) of a study by Erwin Bulte, Lei Pan, Joseph Hella, Gonne Beekman, and Salvatore di Falco that compares two agricultural experiments, one blinded and one unblinded. Bulte et al. find much different results in the two experiments and attribute the difference to expectation effects (when people know they’re receiving an experiment they behave differently); Ozler is skeptical and attributes the different outcomes to various practical differences in implementation of the two experiments. I’m reminded somehow of the notorious sham experiment on the dead chickens, a story that was good for endless discussion in my Bayesian statistics class last semester. I think we can all agree that dead chickens won’t exhibit a placebo effect. Live farmers, though, that’s another story. I don’t have any stake in this particular fight, but on quick reading I’m sympathetic to Ozler’s argument that this all is wel

2 andrew gelman stats-2012-12-30-Fixed effects, followed by Bayes shrinkage?

Introduction: Stuart Buck writes: I have a question about fixed effects vs. random effects . Amongst economists who study teacher value-added, it has become common to see people saying that they estimated teacher fixed effects (via least squares dummy variables, so that there is a parameter for each teacher), but that they then applied empirical Bayes shrinkage so that the teacher effects are brought closer to the mean. (See this paper by Jacob and Lefgren, for example.) Can that really be what they are doing? Why wouldn’t they just run random (modeled) effects in the first place? I feel like there’s something I’m missing. My reply: I don’t know the full story here, but I’m thinking there are two goals, first to get an unbiased estimate of an overall treatment effect (and there the econometricians prefer so-called fixed effects; I disagree with them on this but I know where they’re coming from) and second to estimate individual teacher effects (and there it makes sense to use so-called

3 andrew gelman stats-2012-12-29-Sexism in science (as elsewhere)

Introduction: Solomon Hsiang sends along this from Corinne Moss-Racusin, John Dovidio, Victoria Brescoll, Mark Graham, and Jo Handelsman: Despite efforts to recruit and retain more women, a stark gender disparity persists within academic science. . . . In a randomized double-blind study . . . science faculty from research-intensive universities rated the application materials of a student—who was randomly assigned either a male or female name—for a laboratory manager position. Faculty participants rated the male applicant as significantly more competent and hireable than the (identical) female applicant. These participants also selected a higher starting salary and offered more career mentoring to the male applicant. . . . I hate to talk about things like this since presumably I’m a beneficiary. But now that I’ve climbed the ladder myself I suppose I’m not at any risk. I don’t know anything much about lab manager positions—that’s more something you’d see in a biology department—but I do

4 andrew gelman stats-2012-12-28-New book by Stef van Buuren on missing-data imputation looks really good!

Introduction: Ben points us to a new book, Flexible Imputation of Missing Data . It’s excellent and I highly recommend it. Definitely worth the $89.95. Van Buuren’s book is great even if you don’t end up using the algorithm described in the book (I actually like their approach but I do think there are some limitations with their particular implementation, which is one reason we’re developing our own package ); he supplies lots of intuition, examples, and graphs. P.S. Stef’s book features an introduction by Don Rubin, which gets me thinking: if Don can find the time to write an introduction to somebody else’s book, he surely should be willing to read and comment on the third edition of his own book, no?

5 andrew gelman stats-2012-12-27-The Möbius strip, or, marketing that is impervious to criticism

Introduction: Johnny Carson had this great trick where, after a joke bombed, he’d do such a good double-take that he’d end up getting a huge laugh. This gimmick could never have worked as his sole shtick—at some point, Johnny had to tell some good jokes—but it was a reliable way to limit the downside. For the purpose of our discussion here, the point is that, even when the joke failed, Carson had a way out. I thought of this today after following a link from a commenter that led to this blog on publicity-minded author Tim Ferriss. I’ve never read anything by Ferriss but I’ve read about him on occasion: his gimmick is he promotes his book using ingenious marketing strategies. Sort of like how Madonna is famous for being famous, and Paris Hilton is famous for being famous for being famous, Ferriss is famous for self-promotion. Matt Metzgar writes : I [Metzgar] saw a bunch of ads on the internet today for Tim Ferriss’ new book. Even though the book was released today, it already has all

6 andrew gelman stats-2012-12-26-What do people do wrong? WSJ columnist is looking for examples!

Introduction: Carl Bialik of the Wall Street Journal writes: I’m working on a column this week about numerical/statistical tips and resolutions for writers and people in other fields in the new year. 2013 is the International Year of Statistics, so I’d like to offer some ways to better grapple with statistics in the year ahead. Here’s where you come in. If you have time in the next couple of days, please send me an idea or three about what people often do incorrectly when it comes to numbers, and how they could do better, without making things much more complicated. Maybe with an example of something you’ve seen that rubbed you the wrong way, and how you’d fix it. Bonus if you can tie it in to some sort of statistic that will be particularly relevant in 2013. Any ideas of yours I use, I’ll credit, of course. Examples of what I have in mind: –Don’t report on how ubiquitous or important something is by saying there are 130,000 Google search results for it. Or if you do, at least check that yo

7 andrew gelman stats-2012-12-26-Impersonators

Introduction: This story of a Cindy Sherman impersonator reminded me of some graffiti I saw in a bathroom of the Whitney Museum many years ago. My friend Kenny and I had gone there for the Biennial which had an exhibit featuring Keith Haring and others of the neo-taggers (or whatever they were called). The bathroom walls were all painted over by Kenny Scharf [no relation to my friend] in his characteristically irritating doodle style. On top of the ugly stylized graffiti was a Sharpie’d scrawl: “Kenny Scharf is a pretentious asshole.” I suspected this last bit was added by someone else, but maybe it was Scharf himself? Ira Glass is a bigshot and can get Cindy Sherman on the phone, but I was just some guy, all I could do was write Scharf a letter, c/o the Whitney Museum. I described the situation and asked if he was the one who had written, “Kenny Scharf is a pretentious asshole.” He did not reply.

8 andrew gelman stats-2012-12-25-Diving chess

Introduction: Knowing of my interest in Turing run-around-the-house chess , David Lockhart points me to this : Diving Chess is a chess variant, which is played in a swimming pool. Instead of using chess clocks, each player must submerge themselves underwater during their turn, only to resurface when they are ready to make a move. Players must make a move within 5 seconds of resurfacing (they will receive a warning if not, and three warnings will result in a forfeit). Diving Chess was invented by American Chess Master Etan Ilfeld; the very first exhibition game took place between Ilfeld and former British Chess Champion William Hartston at the Thirdspace gym in Soho on August 2nd, 2011. Hartston won the match which lasted almost two hours such that each player was underwater for an entire hour.

9 andrew gelman stats-2012-12-24-Textbook for data visualization?

Introduction: Dave Choi writes: I’m building a course called “Exploring and visualizing data,” for Heinz college in Carnegie Mellon (public policy and information systems). Do you know any books that might be good for such a course? I’m hoping to get non-statisticians to appreciate the statistician’s point of view on this subject. I immediately thought of Bill Cleveland’s 1985 classic, The Elements of Graphing Data, but I wasn’t sure of what comes next. There are a lot of books on how to make graphics in R, but I’m not quite sure that’s the point. And I’m loath to recommend Tufte since it would be kinda scary if a student were to take all of his ideas too seriously. Any suggestions?

10 andrew gelman stats-2012-12-23-Peter Bartlett on model complexity and sample size

Introduction: Zach Shahn saw this and writes: I just heard a talk by Peter Bartlett about model selection in “unlimited” data situations that essentially addresses this curve. He talks about the problem of model selection given a computational budget (rather than given a sample size). You can either use your computational budget to get more data or fit a more complex model. He shows that you can get oracle inequalities for model selection algorithms under this paradigm (as long as the candidate models are nested). I can’t follow all the details but it looks cool! This is what they should be teaching in theoretical statistics class, instead of sufficient statistics and the Neyman-Pearson lemma and all that other old stuff. Zach also asks: I have a question about political science. I always hear that the direction of the economy is one of the best predictors of election outcome. What’s your thinking about the causal mechanism(s) behind the success of economic trend indicators as pr

11 andrew gelman stats-2012-12-22-More Pinker Pinker Pinker

Introduction: After I posted this recent comment on a blog of Steven Pinker (see also here ), we had the following exchange. I’m reposting it here (with Pinker’s agreement) not because we achieved any deep insights but because I thought it useful to reveal to people that so-called experts such as us are not so clear on these issues either. AG: I noticed your article on red and blue states and had some thoughts. . . . The short summary is that I think that your idea is interesting but that, as stated, it explains too much, in that your story is based on centuries-long history but it only fits electoral patterns since the 1980s. SP: Though the exact alignment between red and blue states, political parties, and particular issues surely shift, I’d be surprised if the basic alignments between geography and the right-left divide, and the issues that cluster on each side of the divide, have radically changed over the past century. (Obviously if you define “red” and “blue” by the Republican

12 andrew gelman stats-2012-12-21-Two reviews of Nate Silver’s new book, from Kaiser Fung and Cathy O’Neil

Introduction: People keep asking me what I think of Nate’s book, and I keep replying that, as a blogger, I’m spoiled. I’m so used to getting books for free that I wouldn’t go out and buy a book just for the purpose of reviewing it. (That reminds me that I should post reviews of some of those books I’ve received in the mail over the past few months.) I have, however, encountered a couple of reviews of The Signal and the Noise so I thought I’d pass them on to you. Both these reviews are by statisticians / data scientists who work here in NYC in the non-academic “real world” so in that sense they are perhaps better situated than me to review the book (also, they have not collaborated with Nate so they have no conflict of interest). Kaiser Fung gives a positive review : It is in the subtitle—“why so many predictions fail – but some don’t”—that one learns the core philosophy of Silver: he is most concerned with the honest evaluation of the performance of predictive models. The failure to look

13 andrew gelman stats-2012-12-21-Kahan on Pinker on politics

Introduction: Reacting to my recent post on Steven Pinker’s too-broad (in my opinion) speculations on red and blue states, Dan “cultural cognition” Kahan writes : Pinker is clearly right to note that mass political opinions on seemingly diverse issues cohere, and Andrew, I think, is way too quick to challenge this I [Kahan] could cite to billions of interesting papers, but I’ll just show you what I mean instead. A recent CCP data collection involving a nationally representative on-line sample of 1750 subjects included a module that asked the subjects to indicate on a six-point scale “how strongly . . . you support or oppose” a collection of policies: policy_gun Stricter gun control laws in the United States. policy_healthcare Universal health care. policy_taxcut Raising income taxes for persons in the highest-income tax bracket. policy_affirmative action Affirmative action for minorities. policy_warming Stricter carbon emission standards to reduce global warming. Positions c

14 andrew gelman stats-2012-12-20-Who exactly are those silly academics who aren’t as smart as a Vegas bookie?

Introduction: I get suspicious when I hear unsourced claims that unnamed experts somewhere are making foolish statements. For example, I recently came across this, from a Super Bowl-themed article from 2006 by Stephen Dubner and Steven Levitt: As it happens, there is one betting strategy that will routinely beat a bookie, and you don’t even have to be smart to use it. One of the most undervalued N.F.L. bets is the home underdog — a team favored to lose but playing in its home stadium. If you had bet $5,000 on the home underdog in every N.F.L. game over the past two decades, you would be up about $150,000 by now (a winning rate of roughly 53 percent). So far, so good. I wonder if this pattern still holds. But then Dubner and Levitt continue: This fact has led some academics to conclude that bookmakers simply aren’t very smart. If an academic researcher can find this loophole, shouldn’t a professional bookie be able to? But the fact is most bookies are doing just fine. So could it be

15 andrew gelman stats-2012-12-19-Steven Pinker is a psychologist who writes on politics. His theories are interesting but are framed too universally to be valid

Introduction: Psychology is a universal science of human nature, whereas political science is centered on the study of particular historical events and trends. Perhaps it is unsurprising, then, that when a psychologist looks at politics, he presents ideas that are thought-provoking but are too general to quite work. This is fine; political scientists can then take such ideas and try to adapt them more closely to particular circumstances. The psychologist I’m thinking about here is Steven Pinker, who, in writes the following on the question, “Why Are States So Red and Blue?”: But why do ideology and geography cluster so predictably? Why, if you know a person’s position on gay marriage, can you predict that he or she will want to increase the military budget and decrease the tax rate . . . there may also be coherent mindsets beneath the diverse opinions that hang together in right-wing and left-wing belief systems. Political philosophers have long known that the ideologies are rooted in diffe

16 andrew gelman stats-2012-12-18-Postdoc positions at Microsoft Research – NYC

Introduction: Sharad Goel sends this in: Microsoft Research NYC [ http://research.microsoft.com/newyork/ ] seeks outstanding applicants for 2-year postdoctoral researcher positions. We welcome applicants with a strong academic record in one of the following areas: * Computational social science: http://research.microsoft.com/cssnyc * Online experimental social science: http://research.microsoft.com/oess_nyc * Algorithmic economics and market design: http://research.microsoft.com/algorithmic-economics/ * Machine learning: http://research.microsoft.com/mlnyc/ We will also consider applicants in other focus areas of the lab, including information retrieval, and behavioral & empirical economics. Additional information about these areas is included below. Please submit all application materials by January 11, 2013. ———- COMPUTATIONAL SOCIAL SCIENCE http://research.microsoft.com/cssnyc With an increasing amount of data on every aspect of our daily activities — from what we buy, to wh

17 andrew gelman stats-2012-12-18-It happened in Connecticut

Introduction: From the sister blog, some reasons why the political reaction might be different this time.

18 andrew gelman stats-2012-12-17-Statistics in a world where nothing is random

Introduction: Rama Ganesan writes: I think I am having an existential crisis. I used to work with animals (rats, mice, gerbils etc.) Then I started to work in marketing research where we did have some kind of random sampling procedure. So up until a few years ago, I was sort of okay. Now I am teaching marketing research, and I feel like there is no real random sampling anymore. I take pains to get students to understand what random means, and then the whole lot of inferential statistics. Then almost anything they do – the sample is not random. They think I am contradicting myself. They use convenience samples at every turn – for their school work, and the enormous amount on online surveying that gets done. Do you have any suggestions for me? Other than say, something like this . My reply: Statistics does not require randomness. The three essential elements of statistics are measurement, comparison, and variation. Randomness is one way to supply variation, and it’s one way to model

19 andrew gelman stats-2012-12-17-Stan and RStan 1.1.0

Introduction: We’re happy to announce the availability of Stan and RStan versions 1.1.0, which are general tools for performing model-based Bayesian inference using the no-U-turn sampler, an adaptive form of Hamiltonian Monte Carlo. Information on downloading and installing and using them is available as always from Stan Home Page: http://mc-stan.org/ Let us know if you have any problems on the mailing lists or at the e-mails linked on the home page (please don’t use this web page). The full release notes follow. (R)Stan Version 1.1.0 Release Notes =================================== -- Backward Compatibility Issue * Categorical distribution recoded to match documentation; it now has support {1,...,K} rather than {0,...,K-1}. * (RStan) change default value of permuted flag from FALSE to TRUE for Stan fit S4 extract() method -- New Features * Conditional (if-then-else) statements * While statements -- New Functions * generalized multiply_lower_tri

20 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