andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-921 knowledge-graph by maker-knowledge-mining
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Introduction: Nowadays “Bayesian” is often taken to be a synonym for rationality, and I can see how this can irritate thoughtful philosophers and statisticians alike: To start with, lots of rational thinking—even lots of rational statistical inference—does not occur within the Bayesian formalism. And, to look at it from the other direction, lots of self-proclaimed Bayesian inference hardly seems rational at all. And in what way is “subjective probability” a model for rational scientific inquiry? On the contrary, subjectivity and rationality are in many ways opposites! [emphasis added] The goal of this paper is to break the link between Bayesian modeling (good, in my opinion) and subjectivity (bad). From this perspective, the irritation of falsificationists regarding exaggerated claims of Bayesian rationality are my ally. . . . See here for the full article, to appear in the journal Rationality, Markets and Morals.
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1 Nowadays “Bayesian” is often taken to be a synonym for rationality, and I can see how this can irritate thoughtful philosophers and statisticians alike: To start with, lots of rational thinking—even lots of rational statistical inference—does not occur within the Bayesian formalism. [sent-1, score-2.213]
2 And, to look at it from the other direction, lots of self-proclaimed Bayesian inference hardly seems rational at all. [sent-2, score-0.833]
3 And in what way is “subjective probability” a model for rational scientific inquiry? [sent-3, score-0.478]
4 On the contrary, subjectivity and rationality are in many ways opposites! [sent-4, score-0.803]
5 [emphasis added] The goal of this paper is to break the link between Bayesian modeling (good, in my opinion) and subjectivity (bad). [sent-5, score-0.602]
6 From this perspective, the irritation of falsificationists regarding exaggerated claims of Bayesian rationality are my ally. [sent-6, score-1.059]
7 See here for the full article, to appear in the journal Rationality, Markets and Morals. [sent-10, score-0.197]
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same-blog 1 1.0000001 921 andrew gelman stats-2011-09-23-That odd couple, “subjectivity” and “rationality”
Introduction: Nowadays “Bayesian” is often taken to be a synonym for rationality, and I can see how this can irritate thoughtful philosophers and statisticians alike: To start with, lots of rational thinking—even lots of rational statistical inference—does not occur within the Bayesian formalism. And, to look at it from the other direction, lots of self-proclaimed Bayesian inference hardly seems rational at all. And in what way is “subjective probability” a model for rational scientific inquiry? On the contrary, subjectivity and rationality are in many ways opposites! [emphasis added] The goal of this paper is to break the link between Bayesian modeling (good, in my opinion) and subjectivity (bad). From this perspective, the irritation of falsificationists regarding exaggerated claims of Bayesian rationality are my ally. . . . See here for the full article, to appear in the journal Rationality, Markets and Morals.
2 0.32579774 692 andrew gelman stats-2011-05-03-“Rationality” reinforces, does not compete with, other models of behavior
Introduction: John Sides followed up on a discussion of his earlier claim that political independents vote for president in a reasonable way based on economic performance. John’s original post led to the amazing claim by New Republic writer Jonathan Chait that John wouldn’t “even want to be friends with anybody who” voted in this manner. I’ve been sensitive to discussions of rationality and voting ever since Aaron Edlin, Noah Kaplan, and I wrote our article on voting as a rational choice: why and how people vote to improve the well-being of others. Models of rationality are controversial In politics, just as they are in other fields ranging from economics to criminology. On one side you have people trying to argue that all behavior is rational, from lottery playing to drug addiction to engaging in email with exiled Nigerian royalty. Probably the only behavior that nobody has yet to claim is rational is blogging, but I bet that’s coming too. From the other direction, lots of people poi
Introduction: I think I’m starting to resolve a puzzle that’s been bugging me for awhile. Pop economists (or, at least, pop micro-economists) are often making one of two arguments: 1. People are rational and respond to incentives. Behavior that looks irrational is actually completely rational once you think like an economist. 2. People are irrational and they need economists, with their open minds, to show them how to be rational and efficient. Argument 1 is associated with “why do they do that?” sorts of puzzles. Why do they charge so much for candy at the movie theater, why are airline ticket prices such a mess, why are people drug addicts, etc. The usual answer is that there’s some rational reason for what seems like silly or self-destructive behavior. Argument 2 is associated with “we can do better” claims such as why we should fire 80% of public-schools teachers or Moneyball-style stories about how some clever entrepreneur has made a zillion dollars by exploiting some inefficienc
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Introduction: Philosopher L. A. Paul and sociologist Kieran Healy write : Choosing to have a child involves a leap of faith, not a carefully calibrated rational choice. When surprising results surface about the dissatisfaction many parents experience, telling yourself that you knew it wouldn’t be that way for you is simply a rationalization. The same is true if you tell yourself you know you’re happier not being a parent. The standard story of parenthood says it’s a deeply fulfilling event that is like nothing else you’ve ever experienced, and that you should carefully weigh what it will be like before choosing to do it. But in reality you can’t have it both ways. I disagree that you can’t have it both ways, for three reasons: 1. Many potential parents do have an idea of what it will be like to be a parent, having participated in child care as an older sibling, aunt, or uncle. 2. The decision of whether to have a child occurs many times: the decision of whether to have a second child
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Introduction: Kent Osband writes: I just read your article The holes in my philosophy of Bayesian data analysis . I agree on the importance of what you flagged as “comparable incoherence in all other statistical philosophies”. The problem arises when a string of unexpected observations persuades that one’s original structural hypothesis (which might be viewed as a parameter describing the type of statistical relationship) was false. However, I would phrase this more positively. Your Bayesian prior actually cedes alternative structural hypotheses, albeit with tiny epsilon weights. Otherwise you would never change your mind. However, these epsilons are so difficult to measure, and small differences can have such a significant impact on speed of adjustment (as in the example in Chapter 7 of Pandora’s Risk), that effectively we all look incoherent. This is a prime example of rational turbulence. Rational turbulence can arise even without a structural break. Any time new evidence arrives that
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Introduction: Nowadays “Bayesian” is often taken to be a synonym for rationality, and I can see how this can irritate thoughtful philosophers and statisticians alike: To start with, lots of rational thinking—even lots of rational statistical inference—does not occur within the Bayesian formalism. And, to look at it from the other direction, lots of self-proclaimed Bayesian inference hardly seems rational at all. And in what way is “subjective probability” a model for rational scientific inquiry? On the contrary, subjectivity and rationality are in many ways opposites! [emphasis added] The goal of this paper is to break the link between Bayesian modeling (good, in my opinion) and subjectivity (bad). From this perspective, the irritation of falsificationists regarding exaggerated claims of Bayesian rationality are my ally. . . . See here for the full article, to appear in the journal Rationality, Markets and Morals.
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Introduction: Kent Osband writes: I just read your article The holes in my philosophy of Bayesian data analysis . I agree on the importance of what you flagged as “comparable incoherence in all other statistical philosophies”. The problem arises when a string of unexpected observations persuades that one’s original structural hypothesis (which might be viewed as a parameter describing the type of statistical relationship) was false. However, I would phrase this more positively. Your Bayesian prior actually cedes alternative structural hypotheses, albeit with tiny epsilon weights. Otherwise you would never change your mind. However, these epsilons are so difficult to measure, and small differences can have such a significant impact on speed of adjustment (as in the example in Chapter 7 of Pandora’s Risk), that effectively we all look incoherent. This is a prime example of rational turbulence. Rational turbulence can arise even without a structural break. Any time new evidence arrives that
3 0.71585065 1259 andrew gelman stats-2012-04-11-How things sound to us, versus how they sound to others
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: Deborah Mayo recommended that I consider coming up with a new name for the statistical methods that I used, given that the term “Bayesian” has all sorts of associations that I dislike (as discussed, for example, in section 1 of this article ). I replied that I agree on Bayesian, I never liked the term and always wanted something better, but I couldn’t think of any convenient alternative. Also, I was finding that Bayesians (even the Bayesians I disagreed with) were reading my research articles, while non-Bayesians were simply ignoring them. So I thought it was best to identify with, and communicate with, those people who were willing to engage with me. More formally, I’m happy defining “Bayesian” as “using inference from the posterior distribution, p(theta|y)”. This says nothing about where the probability distributions come from (thus, no requirement to be “subjective” or “objective”) and it says nothing about the models (thus, no requirement to use the discrete models that hav
Introduction: Updated version of my paper with Xian: The missionary zeal of many Bayesians of old has been matched, in the other direction, by an attitude among some theoreticians that Bayesian methods are absurd—not merely misguided but obviously wrong in principle. We consider several examples, beginning with Feller’s classic text on probability theory and continuing with more recent cases such as the perceived Bayesian nature of the so-called doomsday argument. We analyze in this note the intellectual background behind various misconceptions about Bayesian statistics, without aiming at a complete historical coverage of the reasons for this dismissal. I love this stuff.
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Introduction: Nowadays “Bayesian” is often taken to be a synonym for rationality, and I can see how this can irritate thoughtful philosophers and statisticians alike: To start with, lots of rational thinking—even lots of rational statistical inference—does not occur within the Bayesian formalism. And, to look at it from the other direction, lots of self-proclaimed Bayesian inference hardly seems rational at all. And in what way is “subjective probability” a model for rational scientific inquiry? On the contrary, subjectivity and rationality are in many ways opposites! [emphasis added] The goal of this paper is to break the link between Bayesian modeling (good, in my opinion) and subjectivity (bad). From this perspective, the irritation of falsificationists regarding exaggerated claims of Bayesian rationality are my ally. . . . See here for the full article, to appear in the journal Rationality, Markets and Morals.
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Introduction: Raphael Silberzahn writes: Brian Nosek, Eric Luis Uhlmann, Dan Martin, and I just launched a project through the Open Science Center we think you’ll find interesting. The basic idea is to “Crowdstorm a Dataset”. Multiple independent analysts are recruited to test the same hypothesis on the same data set in whatever manner they see as best. If everyone comes up with the same results, then scientists can speak with one voice. If not, the subjectivity and conditionality of results on analysis strategy is made transparent. For this first project, we are crowdstorming the question of whether soccer referees are more likely to give red cards to dark skin toned players than light skin toned players. The full project description is here . If you’re interested in being one of the crowdstormer analysts, you can register here . All analysts will receive an author credit on the final paper. We would love to have Bayesian analysts represented in the group. Also, please feel free to let
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Introduction: EJ points me to this new techno-thriller . Based on the sentence quoted above, I don’t see it selling lots of copies. It reads like a really boring Raymond Chandler. I still think these two movie ideas would be a better sell.
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Introduction: I. State of the Climate report The National Oceanic and Atmospheric Administration recently released their “State of the Climate Report” for 2009 . The report has chapters discussing global climate (temperatures, water vapor, cloudiness, alpine glaciers,…); oceans (ocean heat content, sea level, sea surface temperatures, etc.); the arctic (sea ice extent, permafrost, vegetation, and so on); Antarctica (weather observations, sea ice extent,…), and regional climates. NOAA also provides a nice page that lets you display any of 11 relevant time-series datasets (land-surface air temperature, sea level, ocean heat content, September arctic sea-ice extent, sea-surface temperature, northern hemisphere snow cover, specific humidity, glacier mass balance, marine air temperature, tropospheric temperature, and stratospheric temperature). Each of the plots overlays data from several databases (not necessarily indepenedent of each other), and you can select which ones to include or leave
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Introduction: Joan Garfield, a leading researcher in statistics education, is conducting a survey of graduate students who teach or assist with the teaching of statistics. She writes: We want to invite them to take a short survey that will enable us to collect some baseline data that we may use in a grant proposal we are developing. The project would provide summer workshops and ongoing support for graduate students who will be teaching or assisting with teaching introductory statistics classes. If the grant is funded, we would invite up to 40 students from around the country who are entering graduate programs in statistics to participate in a three-year training and support program. The goal of this program is to help these students become expert and flexible teachers of statistics, and to support them as they move through their teaching experiences as graduate students. Here’s the the online survey . Garfield writes, “Your responses are completely voluntary and anonymous. Results w
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