Category: Statistics

Against Winner-Take-All Attribution

This is the anti-Wolfram. I did not design or write the Stan language. I’m a user of Stan. Lots of people designed and wrote Stan, most notably Bob Carpenter (designed the language and implemented lots of the algorithms), Matt Hoffman (came up with the Nuts algorithm), and Daniel Lee (put together lots of the internals […]

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“We continuously increased the number of animals until statistical significance was reached to support our conclusions” . . . I think this is not so bad, actually!

For some reason, people have recently been asking me what I think of this journal article which I wrote about months ago . . . so I’ll just repeat my post here: Jordan Anaya pointed me to this post, in which Casper Albers shared this snippet from a recently-published paper from an article in Nature […]

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Robert Heinlein vs. Lawrence Summers

Thomas Ball writes: In this article about Nabokov and the influence of John Dunne’s theories on him (and others in the period l’entre deux guerres) you can see intimations of Borges’ story The Garden of Forking Paths…. The article in question is by Nicholson Baker. Nicholson Baker! It’s great to see that he’s still writing. […]

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StanCon 2018 Helsinki tutorial videos online

StanCon 2018 Helsinki tutorial videos are now online at Stan YouTube channel List of tutorials at StanCon 2018 Helsinki Basics of Bayesian inference and Stan, parts 1 + 2, Jonah Gabry & Lauren Kennedy Hierarchical models, parts 1 + 2, Ben Goodrich Stan C++ development: Adding a new function to Stan, parts 1 + 2, […]

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A.I. parity with the West in 2020

Someone just sent me a link to an editorial by Ken Church, in the journal Natural Language Engineering (who knew that journal was still going? I’d have thought open access would’ve killed it). The abstract of Church’s column says of China, There is a bold government plan for AI with specific milestones for parity with […]

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Hey—take this psychological science replication quiz!

Rob Wilbin writes: I made this quiz where people try to guess ahead of time which results will replicate and which won’t in order to give then a more nuanced understanding of replication issues in psych. Based on this week’s Nature replication paper. It includes quotes and p-values from the original study if people want […]

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John Hattie’s “Visible Learning”: How much should we trust this influential review of education research?

Dan Kumprey, a math teacher at Lake Oswego High School, Oregon, writes: Have you considered taking a look at the book Visible Learning by John Hattie? It seems to be permeating and informing reform in our K-12 schools nationwide. Districts are spending a lot of money sending their staffs to conferences by Solution Tree to […]

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“Identification of and correction for publication bias,” and another discussion of how forking paths is not the same thing as file drawer

Max Kasy and Isaiah Andrews sent along this paper, which begins: Some empirical results are more likely to be published than others. Such selective publication leads to biased estimates and distorted inference. This paper proposes two approaches for identifying the conditional probability of publication as a function of a study’s results, the first based on […]

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3 recent movies from the 50s and the 70s

I’ve been doing some flying, which gives me the opportunity to see various movies on that little seat-back screen. And some of these movies have been pretty good: Logan Lucky. Pure 70s. Kinda like how Stravinsky did those remakes of Tchaikovsky etc. that were cleaner than the original, so did Soderbergh in Logan Lucky, and […]

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RSS 2018 – Significance Tests: Rethinking the Controversy

Day 2, Wednesday 05/09/2018 11:20 – 13:20 Keynote 4 – Significance Tests: Rethinking the Controversy Assembly Room Speakers: Sir David Cox, Nuffield College, Oxford Deborah Mayo, Virginia Tech Richard Morey, Cardiff University Aris Spanos, Virginia Tech Intermingled in today’s statistical controversies are some long-standing, but unresolved, disagreements on the nature and principles of statistical methods […]

Some clues that this study has big big problems

Paul Alper writes: This article from the New York Daily News, reproduced in the Minneapolis Star Tribune, is so terrible in so many ways. Very sad commentary regarding all aspects of statistics education and journalism. The news article, by Joe Dziemianowicz, is called “Study says drinking alcohol is key to living past 90,” with subheading, […]

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Old school

Maciej Cegłowski writes: About two years ago, the Lisp programmer and dot-com millionaire Paul Graham wrote an essay entitled Hackers and Painters, in which he argues that his approach to computer programming is better described by analogies to the visual arts than by the phrase “computer science”. When this essay came out, I was working […]

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“To get started, I suggest coming up with a simple but reasonable model for missingness, then simulate fake complete data followed by a fake missingness pattern, and check that you can recover your missing-data model and your complete data model in that fake-data situation. You can then proceed from there. But if you can’t even do it with fake data, you’re sunk.”

Alex Konkel writes on a topic that never goes out of style: I’m working on a data analysis plan and am hoping you might help clarify something you wrote regarding missing data. I’m somewhat familiar with multiple imputation and some of the available methods, and I’m also becoming more familiar with Bayesian modeling like in […]

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Bayesian model comparison in ecology

Conor Goold writes: I was reading this overview of mixed-effect modeling in ecology, and thought you or your blog readers may be interested in their last conclusion (page 35): Other modelling approaches such as Bayesian inference are available, and allow much greater flexibility in choice of model structure, error structure and link function. However, the […]

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MeDaScIn 2018

The annual Melbourne Data Science Initiative (or MeDaScIn, pronounced medicine) is on again next month (24-27 September) with lots of tutorials, and the annual datathon.
This year there will be a “Forecasting with R” workshop (25 September)…