Posts Tagged ‘ Miscellaneous Statistics ’

Teaching Statistics: A Bag of Tricks (second edition)

April 20, 2017
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Teaching Statistics:  A Bag of Tricks (second edition)

Hey! Deb Nolan and I finished the second edition of our book, Teaching Statistics: A Bag of Tricks. You can pre-order it here. I love love love this book. As William Goldman would say, it’s the “good parts version”: all the fun stuff without the standard boring examples (counting colors of M&M’s, etc.). Great stuff […] The post Teaching Statistics: A Bag of Tricks (second edition) appeared first on Statistical…

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My proposal for JASA: “Journal” = review reports + editors’ recommendations + links to the original paper and updates + post-publication comments

April 19, 2017
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My proposal for JASA:  “Journal” = review reports + editors’ recommendations + links to the original paper and updates + post-publication comments

Whenever they’ve asked me to edit a statistics journal, I say no thank you because I think I can make more of a contribution through this blog. I’ve said no enough times that they’ve stopped asking me. But I’ve had an idea for awhile and now I want to do it. I think that journals […] The post My proposal for JASA: “Journal” = review reports + editors’ recommendations +…

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My talk this Friday in the Machine Learning in Finance workshop

April 18, 2017
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My talk this Friday in the Machine Learning in Finance workshop

This is kinda weird because I don’t know anything about machine learning in finance. I guess the assumption is that statistical ideas are not domain specific. Anyway, here it is: What can we learn from data? Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University The standard framework for statistical inference leads […] The post My talk this Friday in the Machine Learning in Finance workshop appeared…

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The Efron transition? And the wit and wisdom of our statistical elders

April 15, 2017
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The Efron transition?  And the wit and wisdom of our statistical elders

Stephen Martin writes: Brad Efron seems to have transitioned from “Bayes just isn’t as practical” to “Bayes can be useful, but EB is easier” to “Yes, Bayes should be used in the modern day” pretty continuously across three decades. http://www2.stat.duke.edu/courses/Spring10/sta122/Handouts/EfronWhyEveryone.pdf http://projecteuclid.org/download/pdf_1/euclid.ss/1028905930 http://statweb.stanford.edu/~ckirby/brad/other/2009Future.pdf Also, Lindley’s comment in the first article is just GOLD: “The last example […] The post The Efron transition? And the wit and wisdom of our statistical elders…

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Beyond subjective and objective in statistics: my talk with Christian Hennig tomorrow (Wed) 5pm in London

April 11, 2017
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Christian Hennig and I write: Decisions in statistical data analysis are often justified, criticized, or avoided using concepts of objectivity and subjectivity. We argue that the words “objective” and “subjective” in statistics discourse are used in a mostly unhelpful way, and we propose to replace each of them with broader collections of attributes, with objectivity […] The post Beyond subjective and objective in statistics: my talk with Christian Hennig tomorrow…

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Probability and Statistics in the Study of Voting and Public Opinion (my talk at the Columbia Applied Probability and Risk seminar, 30 Mar at 1pm)

March 27, 2017
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Probability and Statistics in the Study of Voting and Public Opinion Elections have both uncertainty and variation and hence represent a natural application of probability theory. In addition, opinion polling is a classic statistics problem and is featured in just about every course on the topic. But many common intuitions about probability, statistics, and voting […] The post Probability and Statistics in the Study of Voting and Public Opinion (my…

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Some natural solutions to the p-value communication problem—and why they won’t work

March 21, 2017
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Blake McShane and David Gal recently wrote two articles (“Blinding us to the obvious? The effect of statistical training on the evaluation of evidence” and “Statistical significance and the dichotomization of evidence”) on the misunderstandings of p-values that are common even among supposed experts in statistics and applied social research. The key misconception has nothing […] The post Some natural solutions to the p-value communication problem—and why they won’t work…

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Lady in the Mirror

March 14, 2017
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Lady in the Mirror

In the context of a report from a drug study, Stephen Senn writes: The bare facts they established are the following: The International Headache Society recommends the outcome of being pain free two hours after taking a medicine. The outcome of being pain free or having only mild pain at two hours was reported by […] The post Lady in the Mirror appeared first on Statistical Modeling, Causal Inference, and…

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“Beyond Heterogeneity of Effect Sizes”

March 12, 2017
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“Beyond Heterogeneity of Effect Sizes”

Piers Steel writes: One of the primary benefits of meta-analytic syntheses of research findings is that researchers are provided with an estimate of the heterogeneity of effect sizes. . . . Low values for this estimate are typically interpreted as indicating that the strength of an effect generalizes across situations . . . Some have […] The post “Beyond Heterogeneity of Effect Sizes” appeared first on Statistical Modeling, Causal Inference,…

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How is preregistration like random sampling and controlled experimentation

March 9, 2017
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How is preregistration like random sampling and controlled experimentation

In the discussion following my talk yesterday, someone asked about preregistration and I gave an answer that I really liked, something I’d never thought of before. I started with my usual story that preregistration is great in two settings: (a) replicating your own exploratory work (as in the 50 shades of gray paper), and (b) […] The post How is preregistration like random sampling and controlled experimentation appeared first on…

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