andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1575 knowledge-graph by maker-knowledge-mining
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Introduction: John Cook writes : When I hear someone say “personalized medicine” I want to ask “as opposed to what?” All medicine is personalized. If you are in an emergency room with a broken leg and the person next to you is lapsing into a diabetic coma, the two of you will be treated differently. The aim of personalized medicine is to increase the degree of personalization, not to introduce personalization. . . . This to me is a statistical way of thinking, to change an “Is it or isn’t it?” question into a “How much?” question. This distinction arises in many settings but particularly in discussions of causal inference, for example here and here , where I use the “statistical thinking” approach of imagining everything as being on some continuous scale, in contrast to computer scientist Elias Bareinboim and psychology researcher Steven Sloman, both of whom prefer what might be called the “civilian” or “common sense” idea that effects are either real or not, or that certain data can
sentIndex sentText sentNum sentScore
1 If you are in an emergency room with a broken leg and the person next to you is lapsing into a diabetic coma, the two of you will be treated differently. [sent-3, score-0.186]
2 The aim of personalized medicine is to increase the degree of personalization, not to introduce personalization. [sent-4, score-0.373]
3 This to me is a statistical way of thinking, to change an “Is it or isn’t it? [sent-8, score-0.097]
4 My preference for continuous models is closely connected to the partial pooling of Bayesian inference, but I don’t think I have this attitude because I’m a Bayesian. [sent-12, score-0.231]
5 [Hey, look, when you're not paying attention, you slip into non-statistical discrete thinking! [sent-13, score-0.192]
6 ] To put this more formally, I think that my training and experience with Bayesian methods has reinforced my preference for continuity, but in turn my taste in modeling has affected what methods I use. [sent-17, score-0.4]
7 I know, I know, I can't help it, thinking like a human. [sent-20, score-0.122]
8 ] After all, there are lots of discrete Bayesian models out there (Bayes factors, etc. [sent-22, score-0.112]
9 Another example is the use of a numerical measure rather than a yes/no summary (for example, a depression inventory scale rather than a cutoff yielding the distinction “is or is not depressed”). [sent-24, score-0.635]
10 Sure, lexicographic rules are clean and easy to understand, and there are settings where they can approximate or even outperform a weighted average, but we can also get the reverse, a lexicographic rule that’s a complicated mess (see, for example, Figure 6 of this article ). [sent-28, score-0.719]
11 And yet another example is our acceptance of uncertainty. [sent-29, score-0.117]
12 One of the big themes of statistics is that we should be more comfortable admitting what we don’t know, and one of the big problems with many statistical methods as they are applied in practice is that are taken as a way of denying uncertainty. [sent-30, score-0.268]
13 To put it another way, I have two problems with the formulation of statistical tests and conclusions as “true positive,” “true negative,” “false positive,” “false negative. [sent-33, score-0.276]
14 “true”/”false”: In almost all cases of interest, I don’t think the underlying claim is true or false (at least, not in a way that can be directly mapped into a particular statistical model of zero effect, as is generally done. [sent-35, score-0.53]
15 Even if you were to accept the idea that the null hypothesis might be true, I don’t think it’s a good idea to summarize scientific conclusions in this yes/no, significant/not-significant way. [sent-38, score-0.096]
16 Sure, sometimes you really have to make a decision (apply policy A or policy B), but that’s a decision problem. [sent-39, score-0.42]
17 At the inferential stage, I’d prefer to acknowledge my uncertainty. [sent-40, score-0.082]
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Introduction: John Cook writes : When I hear someone say “personalized medicine” I want to ask “as opposed to what?” All medicine is personalized. If you are in an emergency room with a broken leg and the person next to you is lapsing into a diabetic coma, the two of you will be treated differently. The aim of personalized medicine is to increase the degree of personalization, not to introduce personalization. . . . This to me is a statistical way of thinking, to change an “Is it or isn’t it?” question into a “How much?” question. This distinction arises in many settings but particularly in discussions of causal inference, for example here and here , where I use the “statistical thinking” approach of imagining everything as being on some continuous scale, in contrast to computer scientist Elias Bareinboim and psychology researcher Steven Sloman, both of whom prefer what might be called the “civilian” or “common sense” idea that effects are either real or not, or that certain data can
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Introduction: Robert Bell pointed me to this post by Brad De Long on Bayesian statistics, and then I also noticed this from Noah Smith, who wrote: My impression is that although the Bayesian/Frequentist debate is interesting and intellectually fun, there’s really not much “there” there… despite being so-hip-right-now, Bayesian is not the Statistical Jesus. I’m happy to see the discussion going in this direction. Twenty-five years ago or so, when I got into this biz, there were some serious anti-Bayesian attitudes floating around in mainstream statistics. Discussions in the journals sometimes devolved into debates of the form, “Bayesians: knaves or fools?”. You’d get all sorts of free-floating skepticism about any prior distribution at all, even while people were accepting without question (and doing theory on) logistic regressions, proportional hazards models, and all sorts of strong strong models. (In the subfield of survey sampling, various prominent researchers would refuse to mode
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Introduction: This article is a discussion of a paper by Greg Francis for a special issue, edited by E. J. Wagenmakers, of the Journal of Mathematical Psychology. Here’s what I wrote: Much of statistical practice is an effort to reduce or deny variation and uncertainty. The reduction is done through standardization, replication, and other practices of experimental design, with the idea being to isolate and stabilize the quantity being estimated and then average over many cases. Even so, however, uncertainty persists, and statistical hypothesis testing is in many ways an endeavor to deny this, by reporting binary accept/reject decisions. Classical statistical methods produce binary statements, but there is no reason to assume that the world works that way. Expressions such as Type 1 error, Type 2 error, false positive, and so on, are based on a model in which the world is divided into real and non-real effects. To put it another way, I understand the general scientific distinction of real vs
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Introduction: In this discussion from last month, computer science student and Judea Pearl collaborator Elias Barenboim expressed an attitude that hierarchical Bayesian methods might be fine in practice but that they lack theory, that Bayesians can’t succeed in toy problems. I posted a P.S. there which might not have been noticed so I will put it here: I now realize that there is some disagreement about what constitutes a “guarantee.” In one of his comments, Barenboim writes, “the assurance we have that the result must hold as long as the assumptions in the model are correct should be regarded as a guarantee.” In that sense, yes, we have guarantees! It is fundamental to Bayesian inference that the result must hold if the assumptions in the model are correct. We have lots of that in Bayesian Data Analysis (particularly in the first four chapters but implicitly elsewhere as well), and this is also covered in the classic books by Lindley, Jaynes, and others. This sort of guarantee is indeed p
Introduction: Elias Bareinboim asked what I thought about his comment on selection bias in which he referred to a paper by himself and Judea Pearl, “Controlling Selection Bias in Causal Inference.” I replied that I have no problem with what he wrote, but that from my perspective I find it easier to conceptualize such problems in terms of multilevel models. I elaborated on that point in a recent post , “Hierarchical modeling as a framework for extrapolation,” which I think was read by only a few people (I say this because it received only two comments). I don’t think Bareinboim objected to anything I wrote, but like me he is comfortable working within his own framework. He wrote the following to me: In some sense, “not ad hoc” could mean logically consistent. In other words, if one agrees with the assumptions encoded in the model, one must also agree with the conclusions entailed by these assumptions. I am not aware of any other way of doing mathematics. As it turns out, to get causa
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Introduction: John Cook writes : When I hear someone say “personalized medicine” I want to ask “as opposed to what?” All medicine is personalized. If you are in an emergency room with a broken leg and the person next to you is lapsing into a diabetic coma, the two of you will be treated differently. The aim of personalized medicine is to increase the degree of personalization, not to introduce personalization. . . . This to me is a statistical way of thinking, to change an “Is it or isn’t it?” question into a “How much?” question. This distinction arises in many settings but particularly in discussions of causal inference, for example here and here , where I use the “statistical thinking” approach of imagining everything as being on some continuous scale, in contrast to computer scientist Elias Bareinboim and psychology researcher Steven Sloman, both of whom prefer what might be called the “civilian” or “common sense” idea that effects are either real or not, or that certain data can
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Introduction: Steve Ziliak points me to this article by the always-excellent Carl Bialik, slamming hypothesis tests. I only wish Carl had talked with me before so hastily posting, though! I would’ve argued with some of the things in the article. In particular, he writes: Reese and Brad Carlin . . . suggest that Bayesian statistics are a better alternative, because they tackle the probability that the hypothesis is true head-on, and incorporate prior knowledge about the variables involved. Brad Carlin does great work in theory, methods, and applications, and I like the bit about the prior knowledge (although I might prefer the more general phrase “additional information”), but I hate that quote! My quick response is that the hypothesis of zero effect is almost never true! The problem with the significance testing framework–Bayesian or otherwise–is in the obsession with the possibility of an exact zero effect. The real concern is not with zero, it’s with claiming a positive effect whe
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Introduction: John Talbott points me to this , which I briefly mocked a couple months ago. I largely agree with the critics of this research, but I want to reiterate my point from earlier that all the statistical sophistication in the world won’t help you if you’re studying a null effect. This is not to say that the actual effect is zero—who am I to say?—just that the comments about the high-quality statistics in the article don’t say much to me. There’s lots of discussion of the lack of science underlying ESP claims. I can’t offer anything useful on that account (not being a psychologist, I could imagine all sorts of stories about brain waves or whatever), but I would like to point out something that usually doesn’t seem to get mentioned in these discussions, which is that lots of people want to believe in ESP. After all, it would be cool to read minds. (It wouldn’t be so cool, maybe, if other people could read your mind and you couldn’t read theirs, but I suspect most people don’t think
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Introduction: Ben Hyde sends along this : Stuck in the middle of the supplemental data, reporting the total workup for their compounds, was this gem: Emma, please insert NMR data here! where are they? and for this compound, just make up an elemental analysis . . . I’m reminded of our recent discussions of coauthorship, where I argued that I see real advantages to having multiple people taking responsibility for the result. Jay Verkuilen responded: “On the flipside of collaboration . . . is diffusion of responsibility, where everybody thinks someone else ‘has that problem’ and thus things don’t get solved.” That’s what seems to have happened (hilariously) here.
Introduction: Greg Kaplan writes: I noticed that you have blogged a little about interstate migration trends in the US, and thought that you might be interested in a new working paper of mine (joint with Sam Schulhofer-Wohl from the Minneapolis Fed) which I have attached. Briefly, we show that much of the recent reported drop in interstate migration is a statistical artifact: The Census Bureau made an undocumented change in its imputation procedures for missing data in 2006, and this change significantly reduced the number of imputed interstate moves. The change in imputation procedures — not any actual change in migration behavior — explains 90 percent of the reported decrease in interstate migration between the 2005 and 2006 Current Population Surveys, and 42 percent of the decrease between 2000 and 2010. I haven’t had a chance to give a serious look so could only make the quick suggestion to make the graphs smaller and put multiple graphs on a page, This would allow the reader to bett
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Introduction: Andrew Mack writes: There was a brief commentary from the Benetech folk on the Human Security Report Project’s, “The Shrinking Costs of War” report on your blog in January. But the report has since generated a lot of public controversy . Since the report–like the current discussion in your blog on Mike Spagat’s new paper on Iraq–deals with controversies generated by survey-based excess death estimates, we thought your readers might be interested. Our responses to the debate were posted on our website last week. “Shrinking Costs” had discussed the dramatic decline in death tolls from wartime violence since the end of World War II –and its causes. We also argued that deaths from war-exacerbated disease and malnutrition had declined. (The exec. summary is here .) One of the most striking findings was that mortality rates (we used under-five mortality data) decline during most wars. Indeed our latest research indicates that of the total number of years that countries w
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