andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2368 knowledge-graph by maker-knowledge-mining
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Introduction: Charlie Williams writes: As I get interested in Bayesian approaches to statistics, I have one question I wondered if you would find interesting to address at some point on the blog. What does Bayesian work look like in action across a field? From experience, I have some feeling for how ongoing debates evolve (or not) with subsequent studies in response to earlier findings. I wonder if you know how this happens in practice when multiple researchers are using Bayesian approaches. How much are previous findings built into priors? How much advance comes from model improvement? And in a social science field where self-selection and self-interest play a role, how are improved “treatment” effects incorporated and evaluated? I thought you might know of a field where actual back and forth has been carried out mostly in the context of Bayesian analysis or inference, and I thought it would be interesting to take a look at an example as I think about my own field. My reply: I’ve seen Ba
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1 Charlie Williams writes: As I get interested in Bayesian approaches to statistics, I have one question I wondered if you would find interesting to address at some point on the blog. [sent-1, score-0.448]
2 What does Bayesian work look like in action across a field? [sent-2, score-0.228]
3 From experience, I have some feeling for how ongoing debates evolve (or not) with subsequent studies in response to earlier findings. [sent-3, score-0.984]
4 I wonder if you know how this happens in practice when multiple researchers are using Bayesian approaches. [sent-4, score-0.324]
5 And in a social science field where self-selection and self-interest play a role, how are improved “treatment” effects incorporated and evaluated? [sent-7, score-0.693]
6 I thought you might know of a field where actual back and forth has been carried out mostly in the context of Bayesian analysis or inference, and I thought it would be interesting to take a look at an example as I think about my own field. [sent-8, score-1.26]
7 My reply: I’ve seen Bayesian methods used for individual studies, and I’ve seen Bayesian meta-analysis (of course), but I can’t recall seeing an entire field of inquiry placed in a Bayesian perspective, with the posterior inference from an earlier study used as the prior for the next. [sent-9, score-1.753]
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Introduction: Charlie Williams writes: As I get interested in Bayesian approaches to statistics, I have one question I wondered if you would find interesting to address at some point on the blog. What does Bayesian work look like in action across a field? From experience, I have some feeling for how ongoing debates evolve (or not) with subsequent studies in response to earlier findings. I wonder if you know how this happens in practice when multiple researchers are using Bayesian approaches. How much are previous findings built into priors? How much advance comes from model improvement? And in a social science field where self-selection and self-interest play a role, how are improved “treatment” effects incorporated and evaluated? I thought you might know of a field where actual back and forth has been carried out mostly in the context of Bayesian analysis or inference, and I thought it would be interesting to take a look at an example as I think about my own field. My reply: I’ve seen Ba
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Introduction: Bayesian inference, conditional on the model and data, conforms to the likelihood principle. But there is more to Bayesian methods than Bayesian inference. See chapters 6 and 7 of Bayesian Data Analysis for much discussion of this point. It saddens me to see that people are still confused on this issue.
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Introduction: Prasanta Bandyopadhyay and Gordon Brittan write : We introduce a distinction, unnoticed in the literature, between four varieties of objective Bayesianism. What we call ‘strong objective Bayesianism’ is characterized by two claims, that all scientific inference is ‘logical’ and that, given the same background information two agents will ascribe a unique probability to their priors. We think that neither of these claims can be sustained; in this sense, they are ‘dogmatic’. The first fails to recognize that some scientific inference, in particular that concerning evidential relations, is not (in the appropriate sense) logical, the second fails to provide a non-question-begging account of ‘same background information’. We urge that a suitably objective Bayesian account of scientific inference does not require either of the claims. Finally, we argue that Bayesianism needs to be fine-grained in the same way that Bayesians fine-grain their beliefs. I have not read their paper in detai
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Introduction: Deborah Mayo collected some reactions to my recent article , Induction and Deduction in Bayesian Data Analysis. I’m pleased that that everybody (philosopher Mayo, applied statistician Stephen Senn, and theoretical statistician Larry Wasserman) is so positive about my article and that nobody’s defending the sort of hard-core inductivism that’s featured on the Bayesian inference wikipedia page. Here’s the Wikipedia definition, which I disagree with: Bayesian inference uses aspects of the scientific method, which involves collecting evidence that is meant to be consistent or inconsistent with a given hypothesis. As evidence accumulates, the degree of belief in a hypothesis ought to change. With enough evidence, it should become very high or very low. . . . Bayesian inference uses a numerical estimate of the degree of belief in a hypothesis before evidence has been observed and calculates a numerical estimate of the degree of belief in the hypothesis after evidence has been obse
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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: Charlie Williams writes: As I get interested in Bayesian approaches to statistics, I have one question I wondered if you would find interesting to address at some point on the blog. What does Bayesian work look like in action across a field? From experience, I have some feeling for how ongoing debates evolve (or not) with subsequent studies in response to earlier findings. I wonder if you know how this happens in practice when multiple researchers are using Bayesian approaches. How much are previous findings built into priors? How much advance comes from model improvement? And in a social science field where self-selection and self-interest play a role, how are improved “treatment” effects incorporated and evaluated? I thought you might know of a field where actual back and forth has been carried out mostly in the context of Bayesian analysis or inference, and I thought it would be interesting to take a look at an example as I think about my own field. My reply: I’ve seen Ba
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Introduction: Joel Greenhouse writes: I saw your recent paper on Feller [see here and, for a more fanciful theory, here ]. Looks like it was fun to write. I recently wrote a paper that asks an orthogonal question to yours. Why during the 1950-1960′s did Jerry Cornfield become a Bayesian? It appeared in Statistics in Medicine – “On becoming a Bayesian: Early correspondences between J. Cornfield and L. J. Savage.” In his paper, Greenhouse writes: Jerome Cornfield was arguably the leading proponent for the use of Bayesian methods in biostatistics during the 1960s. Prior to 1963, however, Cornfield had no publications in the area of Bayesian statistics. At a time when frequentist methods were the dominant influence on statistical practice, Cornfield went against the mainstream and embraced Bayes. . . . Cornfield’s interest in Bayesian methods began prior to 1961 and that the clarity of his Bayesian outlook began to take shape following Birnbaum’s ASA paper on the likelihood prin- cip
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Introduction: Continuing with my discussion of the articles in the special issue of the journal Rationality, Markets and Morals on the philosophy of Bayesian statistics: Stephen Senn, “You May Believe You Are a Bayesian But You Are Probably Wrong”: I agree with Senn’s comments on the impossibility of the de Finetti subjective Bayesian approach. As I wrote in 2008, if you could really construct a subjective prior you believe in, why not just look at the data and write down your subjective posterior. The immense practical difficulties with any serious system of inference render it absurd to think that it would be possible to just write down a probability distribution to represent uncertainty. I wish, however, that Senn would recognize my Bayesian approach (which is also that of John Carlin, Hal Stern, Don Rubin, and, I believe, others). De Finetti is no longer around, but we are! I have to admit that my own Bayesian views and practices have changed. In particular, I resonate wit
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Introduction: I came across this article on the philosophy of statistics by University of Michigan economist John DiNardo. I don’t have much to say about the substance of the article because most of it is an argument against something called “Bayesian methods” that doesn’t have much in common with the Bayesian data analysis that I do. If an quantitative, empirically-minded economist at a top university doesn’t know about modern Bayesian methods, then it’s a pretty good guess that confusion holds in many other quarters as well, so I thought I’d try to clear a couple of things up. (See also here .) In the short term, I know I have some readers at the University of Michigan, so maybe a couple of you could go over to Prof. DiNardo’s office and discuss this with him? For the rest of you, please spread the word. My point here is not to claim that DiNardo should be using Bayesian methods or to claim that he’s doing anything wrong in his applied work. It’s just that he’s fighting against a bu
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Introduction: Hykel Hosni noticed this bit from the Lindley Prize page of the Society for Bayesan Analysis: Lindley became a great missionary for the Bayesian gospel. The atmosphere of the Bayesian revival is captured in a comment by Rivett on Lindley’s move to University College London and the premier chair of statistics in Britain: “it was as though a Jehovah’s Witness had been elected Pope.” From my perspective, this was amusing (if commonplace): a group of rationalists jocularly characterizing themselves as religious fanatics. And some of this is in response to intense opposition from outsiders (see the Background section here ). That’s my view. I’m an insider, a statistician who’s heard all jokes about religious Bayesians, from Bayesian and non-Bayesian statisticians alike. But Hosni is an outsider, and here’s how he sees the above-quoted paragraph: Research, however, is not a matter of faith but a matter of arguments, which should always be evaluated with the utmost intellec
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Introduction: Charlie Williams writes: As I get interested in Bayesian approaches to statistics, I have one question I wondered if you would find interesting to address at some point on the blog. What does Bayesian work look like in action across a field? From experience, I have some feeling for how ongoing debates evolve (or not) with subsequent studies in response to earlier findings. I wonder if you know how this happens in practice when multiple researchers are using Bayesian approaches. How much are previous findings built into priors? How much advance comes from model improvement? And in a social science field where self-selection and self-interest play a role, how are improved “treatment” effects incorporated and evaluated? I thought you might know of a field where actual back and forth has been carried out mostly in the context of Bayesian analysis or inference, and I thought it would be interesting to take a look at an example as I think about my own field. My reply: I’ve seen Ba
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Introduction: Yair points us to this page full of wonderful graphs from the Stephen Wolfram blog. Here are a few: And some words: People talk less about video games as they get older, and more about politics and the weather. Men typically talk more about sports and technology than women—and, somewhat surprisingly to me, they also talk more about movies, television and music. Women talk more about pets+animals, family+friends, relationships—and, at least after they reach child-bearing years, health. . . . Some of this is rather depressingly stereotypical. And most of it isn’t terribly surprising to anyone who’s known a reasonable diversity of people of different ages. But what to me is remarkable is how we can see everything laid out in such quantitative detail in the pictures above—kind of a signature of people’s thinking as they go through life. Of course, the pictures above are all based on aggregate data, carefully anonymized. But if we start looking at individuals, we’ll s
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Introduction: I, like Steve Hsu , I too would love to read a definitive biography of John von Neumann (or, as we’d say in the U.S., “John Neumann”). I’ve read little things about him in various places such as Stanislaw Ulam’s classic autobiography, and two things I’ve repeatedly noticed are: 1. Neumann comes off as a obnoxious, self-satisfied jerk. He just seems like the kind of guy I wouldn’t like in real life. 2. All these great men seem to really have loved the guy. It’s hard for me to reconcile two impressions above. Of course, lots of people have a good side and a bad side, but what’s striking here is that my impressions of Neumann’s bad side come from the very stories that his friends use to demonstrate how lovable he was! So, yes, I’d like to see the biography–but only if it could resolve this paradox. Also, I don’t know how relevant this is, but Neumann shares one thing with the more-lovable Ulam and the less-lovable Mandelbrot: all had Jewish backgrounds but didn’t seem to
4 0.97102332 2137 andrew gelman stats-2013-12-17-Replication backlash
Introduction: Raghuveer Parthasarathy pointed me to an article in Nature by Mina Bissell, who writes , “The push to replicate findings could shelve promising research and unfairly damage the reputations of careful, meticulous scientists.” I can see where she’s coming from: if you work hard day after day in the lab, it’s gotta be a bit frustrating to find all your work questioned, for the frauds of the Dr. Anil Pottis and Diederik Stapels to be treated as a reason for everyone else’s work to be considered guilty until proven innocent. That said, I pretty much disagree with Bissell’s article, and really the best thing I can say about it is that I think it’s a good sign that the push for replication is so strong that now there’s a backlash against it. Traditionally, leading scientists have been able to simply ignore the push for replication. If they are feeling that the replication movement is strong enough that they need to fight it, that to me is good news. I’ll explain a bit in the conte
Introduction: Last month we discussed an opinion piece by Mina Bissell, a nationally-recognized leader in cancer biology. Bissell argued that there was too much of a push to replicate scientific findings. I disagreed , arguing that scientists should want others to be able to replicate their research, that it’s in everyone’s interest if replication can be done as fast and reliably as possible, and that if a published finding cannot be easily replicated, this is at best a failure of communication (in that the conditions for successful replication have not clearly been expressed), or possibly a fragile finding (that is, a phenomenon that appears under some conditions but not others), or at worst a plain old mistake (possibly associated with lab error or maybe with statistical error of some sort, such as jumping to certainty based on a statistically significant claim that arose from multiple comparisons ). So we disagreed. Fair enough. But I got to thinking about a possible source of our diffe
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