Posts Tagged ‘ Miscellaneous Statistics ’

Using black-box machine learning predictions as inputs to a Bayesian analysis

September 20, 2017
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Following up on this discussion [Designing an animal-like brain: black-box “deep learning algorithms” to solve problems, with an (approximately) Bayesian “consciousness” or “executive functioning organ” that attempts to make sense of all these inferences], Mike Betancourt writes: I’m not sure AI (or machine learning) + Bayesian wrapper would address the points raised in the paper. […] The post Using black-box machine learning predictions as inputs to a Bayesian analysis appeared…

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Type M errors in the wild—really the wild!

September 16, 2017
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Type M errors in the wild—really the wild!

Jeremy Fox points me to this article, “Underappreciated problems of low replication in ecological field studies,” by Nathan Lemoine, Ava Hoffman, Andrew Felton, Lauren Baur, Francis Chaves, Jesse Gray, Qiang Yu, and Melinda Smith, who write: The cost and difficulty of manipulative field studies makes low statistical power a pervasive issue throughout most ecological subdisciplines. […] The post Type M errors in the wild—really the wild! appeared first on Statistical…

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Type M errors studied in the wild

September 15, 2017
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Brendan Nyhan points to this article, “Very large treatment effects in randomised trials as an empirical marker to indicate whether subsequent trials are necessary: meta-epidemiological assessment,” by Myura Nagendran, Tiago Pereira, Grace Kiew, Douglas Altman, Mahiben Maruthappu, John Ioannidis, and Peter McCulloch. From the abstract: Objective To examine whether a very large effect (VLE; defined […] The post Type M errors studied in the wild appeared first on Statistical Modeling,…

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It seemed to me that most destruction was being done by those who could not choose between the two

September 12, 2017
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Amateurs, dilettantes, hacks, cowboys, clones — Nick Cave [Note from Dan 11Sept: I wanted to leave some clear air after the StanCon reminder, so I scheduled this post for tomorrow. Which means you get two posts (one from me, one from Andrew) on this in two days. That’s probably more than the gay face study deserves.] […] The post It seemed to me that most destruction was being done by those…

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God, goons, and gays: 3 quick takes

September 11, 2017
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God, goons, and gays:  3 quick takes

Next open blog spots are in April but all these are topical so I thought I’d throw them down right now for ya. 1. Alex Durante writes: I noticed that this study on how Trump supporters respond to racial cues is getting some media play, notably over at Vox. I was wondering if you have […] The post God, goons, and gays: 3 quick takes appeared first on Statistical Modeling,…

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Gigo update (“electoral integrity project”)

September 5, 2017
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Gigo update (“electoral integrity project”)

Someone sent me this note: I read your takedown of the EIP on Slate and then your original blog post and the P. Norris response. I wanted to offer a couple of points. First, as you can see below, I was asked to be one of the ‘experts.’ I declined. I think we all can […] The post Gigo update (“electoral integrity project”) appeared first on Statistical Modeling, Causal Inference,…

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What to make of reported statistical analysis summaries: Hear no distinction, see no ensembles, speak of no non-random error.

August 31, 2017
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Recently there has been a lot of fuss about the inappropriate interpretations and uses of p-values, significance tests, Bayes factors, confidence intervals, credible intervals and almost anything anyone has ever thought of. That is to desperately discern what to make of reported statistical analysis summaries of individual studies –  largely on their own. Including a credible […] The post What to make of reported statistical analysis summaries: Hear no distinction, see…

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Don’t always give ’em what they want: Practicing scientists want certainty, but I don’t want to offer it to them!

August 22, 2017
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Stephen Senn writes: What the practicing scientist wants to know is what is a good test in practice. I agree with Stephen Senn on most things—even where it seems we disagree, I think we agree on the fundamentals—but in this case I think you have to be careful about giving the practicing scientist what he […] The post Don’t always give ’em what they want: Practicing scientists want certainty, but…

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Two papers and one presentation by Ron Kennett related to workflow

August 22, 2017
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Ron Kennett sent along these two papers: Statistics: A Life Cycle View Aspects of statistical consulting not taught by academia Also this presentation. They’re somewhat relevant to our current project on statistical workflow, so I’m posting them here for convenience. P.S. I used to think it was a good idea to teach statistical consulting, and […] The post Two papers and one presentation by Ron Kennett related to workflow appeared…

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Publish your raw data and your speculations, then let other people do the analysis: track and field edition

August 21, 2017
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There seems to be an expectation in science that the people who gather a dataset should also be the ones who analyze it. But often that doesn’t make sense: what it takes to gather relevant data has little to do with what it takes to perform a reasonable analysis. Indeed, the imperatives of analysis can […] The post Publish your raw data and your speculations, then let other people do…

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