andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1979 knowledge-graph by maker-knowledge-mining
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Introduction: Keith O’Rourke and I wrote an article that begins: Textbooks on statistics emphasize care and precision, via concepts such as reliability and validity in measurement, random sampling and treatment assignment in data collection, and causal identification and bias in estimation. But how do researchers decide what to believe and what to trust when choosing which statistical methods to use? How do they decide the credibility of methods? Statisticians and statistical practitioners seem to rely on a sense of anecdotal evidence based on personal experience and on the attitudes of trusted colleagues. Authorship, reputation, and past experience are thus central to decisions about statistical procedures. It’s for a volume on theoretical or methodological research on authorship, functional roles, reputation, and credibility in social media, edited by Sorin Matei and Elisa Bertino.
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1 Keith O’Rourke and I wrote an article that begins: Textbooks on statistics emphasize care and precision, via concepts such as reliability and validity in measurement, random sampling and treatment assignment in data collection, and causal identification and bias in estimation. [sent-1, score-1.342]
2 But how do researchers decide what to believe and what to trust when choosing which statistical methods to use? [sent-2, score-0.666]
3 Statisticians and statistical practitioners seem to rely on a sense of anecdotal evidence based on personal experience and on the attitudes of trusted colleagues. [sent-4, score-1.027]
4 Authorship, reputation, and past experience are thus central to decisions about statistical procedures. [sent-5, score-0.607]
5 It’s for a volume on theoretical or methodological research on authorship, functional roles, reputation, and credibility in social media, edited by Sorin Matei and Elisa Bertino. [sent-6, score-0.89]
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Introduction: Keith O’Rourke and I wrote an article that begins: Textbooks on statistics emphasize care and precision, via concepts such as reliability and validity in measurement, random sampling and treatment assignment in data collection, and causal identification and bias in estimation. But how do researchers decide what to believe and what to trust when choosing which statistical methods to use? How do they decide the credibility of methods? Statisticians and statistical practitioners seem to rely on a sense of anecdotal evidence based on personal experience and on the attitudes of trusted colleagues. Authorship, reputation, and past experience are thus central to decisions about statistical procedures. It’s for a volume on theoretical or methodological research on authorship, functional roles, reputation, and credibility in social media, edited by Sorin Matei and Elisa Bertino.
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Introduction: Amusing authorship analysis.
Introduction: Arnaud Trolle (no relation ) writes: I have a question about the interpretation of (non-)overlapping of 95% credibility intervals. In a Bayesian ANOVA (a within-subjects one), I computed 95% credibility intervals about the main effects of a factor. I’d like to compare two by two the main effects across the different conditions of the factor. Can I directly interpret the (non-)overlapping of these credibility intervals and make the following statements: “As the 95% credibility intervals do not overlap, both conditions have significantly different main effects” or conversely “As the 95% credibility intervals overlap, the main effects of both conditions are not significantly different, i.e. equivalent”? I heard that, in the case of classical confidence intervals, the second statement is false, but what happens when working within a Bayesian framework? My reply: I think it makes more sense to directly look at inference for the difference. Also, your statements about equivalence
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: I was asked to write an article for the Committee of Presidents of Statistical Societies (COPSS) 50th anniversary volume. Here it is (it’s labeled as “Chapter 1,” which isn’t right; that’s just what came out when I used the template that was supplied). The article begins as follows: The field of statistics continues to be divided into competing schools of thought. In theory one might imagine choosing the uniquely best method for each problem as it arises, but in practice we choose for ourselves (and recom- mend to others) default principles, models, and methods to be used in a wide variety of settings. This article briefly considers the informal criteria we use to decide what methods to use and what principles to apply in statistics problems. And then I follow up with these sections: Statistics: the science of defaults Ways of knowing The pluralist’s dilemma And here’s the concluding paragraph: Statistics is a young science in which progress is being made in many
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Introduction: Keith O’Rourke and I wrote an article that begins: Textbooks on statistics emphasize care and precision, via concepts such as reliability and validity in measurement, random sampling and treatment assignment in data collection, and causal identification and bias in estimation. But how do researchers decide what to believe and what to trust when choosing which statistical methods to use? How do they decide the credibility of methods? Statisticians and statistical practitioners seem to rely on a sense of anecdotal evidence based on personal experience and on the attitudes of trusted colleagues. Authorship, reputation, and past experience are thus central to decisions about statistical procedures. It’s for a volume on theoretical or methodological research on authorship, functional roles, reputation, and credibility in social media, edited by Sorin Matei and Elisa Bertino.
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Introduction: Statisticians take tours in other people’s data. All methods of statistical inference rest on statistical models. Experiments typically have problems with compliance, measurement error, generalizability to the real world, and representativeness of the sample. Surveys typically have problems of undercoverage, nonresponse, and measurement error. Real surveys are done to learn about the general population. But real surveys are not random samples. For another example, consider educational tests: what are they exactly measuring? Nobody knows. Medical research: even if it’s a randomized experiment, the participants in the study won’t be a random sample from the population for whom you’d recommend treatment. You don’t need random sampling to generalize the results of a medical experiment to the general population but you need some substantive theory to make the assumption that effects in your nonrepresentative sample of people will be similar to effects in the population of interest. Ve
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Introduction: I was asked to write an article for the Committee of Presidents of Statistical Societies (COPSS) 50th anniversary volume. Here it is (it’s labeled as “Chapter 1,” which isn’t right; that’s just what came out when I used the template that was supplied). The article begins as follows: The field of statistics continues to be divided into competing schools of thought. In theory one might imagine choosing the uniquely best method for each problem as it arises, but in practice we choose for ourselves (and recom- mend to others) default principles, models, and methods to be used in a wide variety of settings. This article briefly considers the informal criteria we use to decide what methods to use and what principles to apply in statistics problems. And then I follow up with these sections: Statistics: the science of defaults Ways of knowing The pluralist’s dilemma And here’s the concluding paragraph: Statistics is a young science in which progress is being made in many
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Introduction: On statisticians and statistical software: Statisticians are particularly sensitive to default settings, which makes sense considering that statistics is, in many ways, a science based on defaults. What is a “statistical method” if not a recommended default analysis, backed up by some combination of theory and experience?
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Introduction: Keith O’Rourke and I wrote an article that begins: Textbooks on statistics emphasize care and precision, via concepts such as reliability and validity in measurement, random sampling and treatment assignment in data collection, and causal identification and bias in estimation. But how do researchers decide what to believe and what to trust when choosing which statistical methods to use? How do they decide the credibility of methods? Statisticians and statistical practitioners seem to rely on a sense of anecdotal evidence based on personal experience and on the attitudes of trusted colleagues. Authorship, reputation, and past experience are thus central to decisions about statistical procedures. It’s for a volume on theoretical or methodological research on authorship, functional roles, reputation, and credibility in social media, edited by Sorin Matei and Elisa Bertino.
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Introduction: Two people separately sent me this amusing mock-research paper by Brian A. Nosek (I assume that’s what’s meant by “Arina K. Bones”). The article is pretty funny, but this poster (by Nosek and Samuel Gosling) is even better! Check it out: I remarked that this was almost as good as my zombies paper, and my correspondent pointed me to this page of (I assume) Nosek’s research on aliens. P.S. I clicked through to take the test to see if I’m dead or alive, but I got bored after a few minutes. I gotta say, if Gosling can come up with a 10-item measure of the Big Five, this crew should be able to come up with a reasonably valid alive-or-dead test that doesn’t require dozens and dozens of questions!
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Introduction: Amusing authorship analysis.
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