andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2081 knowledge-graph by maker-knowledge-mining
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Introduction: The talk is at the University of Amsterdam in the Diamantbeurs (Weesperplein 4, Amsterdam), room 5.01, at noon. Here’s the plan: Can we use Bayesian methods to resolve the current crisis of statistically-significant research findings that don’t hold up? In recent years, psychology and medicine have been rocked by scandals of research fraud. At the same time, there is a growing awareness of serious flaws in the general practices of statistics for scientific research, to the extent that top journals routinely publish claims that are implausible and cannot be replicated. All this is occurring despite (or perhaps because of?) statistical tools such as Type 1 error control that are supposed to restrict the rate of unreliable claims. We consider ways in which prior information and Bayesian methods might help resolve these problems. I don’t know how organized this talk will be. It combines a bunch of ideas that have been floating around recently. Here are a few recent articles that
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1 The talk is at the University of Amsterdam in the Diamantbeurs (Weesperplein 4, Amsterdam), room 5. [sent-1, score-0.062]
2 Here’s the plan: Can we use Bayesian methods to resolve the current crisis of statistically-significant research findings that don’t hold up? [sent-3, score-0.298]
3 In recent years, psychology and medicine have been rocked by scandals of research fraud. [sent-4, score-0.246]
4 At the same time, there is a growing awareness of serious flaws in the general practices of statistics for scientific research, to the extent that top journals routinely publish claims that are implausible and cannot be replicated. [sent-5, score-0.1]
5 ) statistical tools such as Type 1 error control that are supposed to restrict the rate of unreliable claims. [sent-7, score-0.097]
6 We consider ways in which prior information and Bayesian methods might help resolve these problems. [sent-8, score-0.135]
7 It combines a bunch of ideas that have been floating around recently. [sent-10, score-0.049]
8 (Andrew Gelman and Thomas Basboll) [2014] The AAA tranche of subprime science. [sent-14, score-0.115]
9 (Andrew Gelman and Eric Loken) [2014] The connection between varying treatment effects and the crisis of unreplicable research: A Bayesian perspective. [sent-16, score-0.126]
10 (Andrew Gelman) [2013] Is it possible to be an ethicist without being mean to people? [sent-18, score-0.059]
11 (Andrew Gelman) [2013] It’s too hard to publish criticisms and obtain data for replication. [sent-20, score-0.054]
12 For a volume on theoretical or methodological research on authorship, functional roles, reputation, and credibility on social media, ed. [sent-23, score-0.086]
13 (Andrew Gelman and Keith O’Rourke) [2013] To throw away data: Plagiarism as a statistical crime. [sent-25, score-0.048]
14 (Andrew Gelman) [2012] P-values and statistical practice. [sent-35, score-0.048]
15 (Andrew Gelman) [2012] Ethics and the statistical use of prior information. [sent-37, score-0.048]
16 (Andrew Gelman) Design analysis, prospective or retrospective, using external information. [sent-39, score-0.046]
17 (Andrew Gelman and John Carlin) The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time. [sent-40, score-0.236]
18 (Andrew Gelman and Eric Loken) Also a few more that we are in the midst of completing. [sent-41, score-0.05]
19 I’m hoping that giving this talk will help me get these thoughts in order. [sent-42, score-0.062]
20 The slides are here , but lots of my best riffs were in the spoken version only. [sent-47, score-0.11]
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same-blog 1 0.99999994 2081 andrew gelman stats-2013-10-29-My talk in Amsterdam tomorrow (Wed 29 Oct): Can we use Bayesian methods to resolve the current crisis of statistically-significant research findings that don’t hold up?
Introduction: The talk is at the University of Amsterdam in the Diamantbeurs (Weesperplein 4, Amsterdam), room 5.01, at noon. Here’s the plan: Can we use Bayesian methods to resolve the current crisis of statistically-significant research findings that don’t hold up? In recent years, psychology and medicine have been rocked by scandals of research fraud. At the same time, there is a growing awareness of serious flaws in the general practices of statistics for scientific research, to the extent that top journals routinely publish claims that are implausible and cannot be replicated. All this is occurring despite (or perhaps because of?) statistical tools such as Type 1 error control that are supposed to restrict the rate of unreliable claims. We consider ways in which prior information and Bayesian methods might help resolve these problems. I don’t know how organized this talk will be. It combines a bunch of ideas that have been floating around recently. Here are a few recent articles that
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Introduction: There’s lots of overlap but I put each paper into only one category. Also, I’ve included work that has been published in 2013 as well as work that has been completed this year and might appear in 2014 or later. So you can can think of this list as representing roughly two years’ work. Political science: [2014] The twentieth-century reversal: How did the Republican states switch to the Democrats and vice versa? {\em Statistics and Public Policy}. (Andrew Gelman) [2013] Hierarchical models for estimating state and demographic trends in U.S. death penalty public opinion. {\em Journal of the Royal Statistical Society A}. (Kenneth Shirley and Andrew Gelman) [2013] Deep interactions with MRP: Election turnout and voting patterns among small electoral subgroups. {\em American Journal of Political Science}. (Yair Ghitza and Andrew Gelman) [2013] Charles Murray’s {\em Coming Apart} and the measurement of social and political divisions. {\em Statistics, Politics and Policy}.
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Introduction: Philosophie et practique de la statistique bayésienne . I’ll try to update the slides a bit since a few years ago , to add some thoughts I’ve had recently about problems with noninformative priors, even in simple settings. The location of the talk will not be convenient for most of you, but anyone who comes to the trouble of showing up will have the opportunity to laugh at my accent. P.S. For those of you who are interested in the topic but can’t make it to the talk, I recommend these two papers on my non-inductive Bayesian philosophy: [2013] Philosophy and the practice of Bayesian statistics (with discussion). {\em British Journal of Mathematical and Statistical Psychology} {\bf 66}, 8–18. (Andrew Gelman and Cosma Shalizi) [2013] Rejoinder to discussion. (Andrew Gelman and Cosma Shalizi) [2011] Induction and deduction in Bayesian data analysis. {\em Rationality, Markets and Morals}, special topic issue “Statistical Science and Philosophy of Science: Where Do (Should)
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Introduction: Can we use Bayesian methods to resolve the current crisis of unreplicable research? In recent years, psychology and medicine have been rocked by scandals of research fraud. At the same time, there is a growing awareness of serious flaws in the general practices of statistics for scientific research, to the extent that top journals routinely publish claims that cannot be replicated. All this is occurring despite (or perhaps because of?) statistical tools such as Type 1 error control that are supposed to restrict the rate of unreliable claims. We consider ways in which prior information and Bayesian methods might help resolve these problems. Here are the details, and here are the slides from the last time I gave this talk.
Introduction: From August 1990. It was in the form of a note sent to all the people in the statistics group of Bell Labs, where I’d worked that summer. To all: Here’s the abstract of the work I’ve done this summer. It’s stored in the file, /fs5/gelman/abstract.bell, and copies of the Figures 1-3 are on Trevor’s desk. Any comments are of course appreciated; I’m at gelman@stat.berkeley.edu. On the Routine Use of Markov Chains for Simulation Andrew Gelman and Donald Rubin, 6 August 1990 corrected version: 8 August 1990 1. Simulation In probability and statistics we can often specify multivariate distributions many of whose properties we do not fully understand–perhaps, as in the Ising model of statistical physics, we can write the joint density function, up to a multiplicative constant that cannot be expressed in closed form. For an example in statistics, consider the Normal random effects model in the analysis of variance, which can be easily placed in a Bayesian fram
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Introduction: The talk is at the University of Amsterdam in the Diamantbeurs (Weesperplein 4, Amsterdam), room 5.01, at noon. Here’s the plan: Can we use Bayesian methods to resolve the current crisis of statistically-significant research findings that don’t hold up? In recent years, psychology and medicine have been rocked by scandals of research fraud. At the same time, there is a growing awareness of serious flaws in the general practices of statistics for scientific research, to the extent that top journals routinely publish claims that are implausible and cannot be replicated. All this is occurring despite (or perhaps because of?) statistical tools such as Type 1 error control that are supposed to restrict the rate of unreliable claims. We consider ways in which prior information and Bayesian methods might help resolve these problems. I don’t know how organized this talk will be. It combines a bunch of ideas that have been floating around recently. Here are a few recent articles that
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Introduction: “Ich glaube, dass die Wahrscheinlichkeitsrechnung das richtige Werkzeug zum Lösen solcher Probleme ist”, sagt Andrew Gelman , Statistikprofessor von der Columbia-Universität in New York. Wie oft aber derart knifflige Aufgaben im realen Leben auftauchen, könne er nicht sagen. Was fast schon beruhigend klingt. OK, fine.
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Introduction: Philosophie et practique de la statistique bayésienne . I’ll try to update the slides a bit since a few years ago , to add some thoughts I’ve had recently about problems with noninformative priors, even in simple settings. The location of the talk will not be convenient for most of you, but anyone who comes to the trouble of showing up will have the opportunity to laugh at my accent. P.S. For those of you who are interested in the topic but can’t make it to the talk, I recommend these two papers on my non-inductive Bayesian philosophy: [2013] Philosophy and the practice of Bayesian statistics (with discussion). {\em British Journal of Mathematical and Statistical Psychology} {\bf 66}, 8–18. (Andrew Gelman and Cosma Shalizi) [2013] Rejoinder to discussion. (Andrew Gelman and Cosma Shalizi) [2011] Induction and deduction in Bayesian data analysis. {\em Rationality, Markets and Morals}, special topic issue “Statistical Science and Philosophy of Science: Where Do (Should)
Introduction: I was given the opportunity to briefly comment on the paper , A Bayesian approach to complex clinical diagnoses: a case-study in child abuse, by Nicky Best, Deborah Ashby, Frank Dunstan, David Foreman, and Neil McIntosh, for the Journal of the Royal Statistical Society. Here is what I wrote: Best et al. are working on an important applied problem and I have no reason to doubt that their approach is a step forward beyond diagnostic criteria based on point estimation. An attempt at an accurate assessment of variation is important not just for statistical reasons but also because scientists have the duty to convey their uncertainty to the larger world. I am thinking, for example, of discredited claims such as that of the mathematician who claimed to predict divorces with 93% accuracy (Abraham, 2010). Regarding the paper at hand, I thought I would try an experiment in comment-writing. My usual practice is to read the graphs and then go back and clarify any questions through the t
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Introduction: 1. We Bayesian statisticians like to say there are three kinds of statisticians: a. Bayesians; b. People who are Bayesians but don’t realize it (that is, they act in coherence with some unstated probability); c. Failed Bayesians (that is, people whose inference could be improved by some attention to coherence). So, if a statistician does great work, we are inclined to claim this person for the Bayesian cause, even if he or she vehemently denies any Bayesian leanings. 2. In his autobiography, Bertrand Russell tells the story of when he went to prison for opposing World War 1: I [Russell] was much cheered on my arrival by the warden at the gate, who had to take particulars about me. He asked my religion, and I replied ‘agnostic.’ He asked how to spell it, and remarked with a sigh: “Well, there are many religions, but I suppose they all worship the same God.” This remark kept me cheerful for about a week. 3. In an op-ed today, Ross Douthat argues that celebrated a
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Introduction: Felix Salmon relates the story of an economics Nobel Prize winner getting paid by a hedge fund. It would all seems pretty silly—sort of like Coca-Cola featuring Michael Jordan in their ads—except that hedge funds are disreputable nowadays and so it seems vaguely sleazy for a scholar to trade on his academic reputation to make free money in this way. It falls roughly in the same category as that notorious b-school prof in Inside Job who got $125K for writing a b.s. report about the financial stability of Iceland—and then, when they came back to him later and asked how he could’ve written it, he basically said: Hey, I don’t know anything about Iceland, I was just taking their money! That said, if a hedge fund offered me $125K to sit on their board, I’d probably take it! It’s hard to turn down free money. Or maybe not, I don’t really know. So far, when companies have paid me $, it’s been to do something for them, to consult or give a short course. I’d like to think that if
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