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Not-so-recently in the sister blog

The role of covariation versus mechanism information in causal attribution: Traditional approaches to causal attribution propose that information about covariation of factors is used to identify causes of events. In contrast, we present a series of studies showing that people seek out and prefer information about causal mechanisms rather than information about covariation. . . […]

The Feud

I just read the above-titled book by Alex Beam and I really enjoyed it. I’ve been a fan of Beam for a long time; he just has this wonderful equanimous style. The thing that amazes me is that the book got published at all. It’s subtitle is “Vladimir Nabokov, Edmund Wilson, and the End of […]

How to interpret inferential statistics when your data aren’t a random sample

Someone named Adam writes: I’m having a bit of a ‘crisis’ of confidence regarding inferential statistics. I’ve been reading some of the work by Stephen Gorard (e.g. “Against Inferential Statistics”) and David Freedman and Richard Berk (e.g. “Statistical Assumptions as empirical commitments”). These authors appear to be saying this: (1) Inferential statistics assume random sampling […]

A regression puzzle . . . and its solution

Alex Tabarrok writes: Here’s a regression puzzle courtesy of Advanced NFL Stats from a few years ago and pointed to recently by Holden Karnofsky from his interesting new blog, ColdTakes. The nominal issue is how to figure our whether Aaron Rodgers is underpaid or overpaid given data on salaries and expected points added per game. […]

The challenges of statistical measurement . . . in an environment where bad measurement and junk science get hyped

I liked this article by Hannah Fry about the challenges of statistical measurement. This is a topic that many statisticians have ignored, so it’s especially satisfying to see it in the popular press. Fry discusses several examples described in recent books of Deborah Stone and Tim Harford of noisy, biased, or game-able measurements. I agree […]

Default informative priors for effect sizes: Where do they come from?

To coincide with the publication of our article, A Proposal for Informative Default Priors Scaled by the Standard Error of Estimates, Erik van Zwet sends along an explainer. Here’s Erik: 1 Set-up This note is meant as a quick explainer of a set of three pre-prints at The Shrinkage Trilogy. All three have the same […]

Workflow and the role of hypothesis-free data analysis

In our discussion a couple days ago on the role of hypotheses in science, Lakeland wrote: Even “this data is relevant to the question we’re studying” is already a hypothesis. There’s no such thing as hypothesis free data analysis. I’ve sometimes said similar things, in that I like to interpret exploratory graphics as model checks, […]

Theoretical Statistics is the Theory of Applied Statistics: Two perspectives

After watching my video, Theoretical Statistics is the Theory of Applied Statistics: How to Think About What We Do, Ron Kenett points us to these articles: Conceptual Thinking in Statistics and Data Science Education: Interactive Formative Assessment with Meaning Equivalence Reusable Learning Objects (MERLO): Computer age statistics, machine learning, data science and in general, data […]

More on the role of hypotheses in science

Just to be clear before going on: when I say “hypotheses,” I’m talking about scientific hypotheses, which can at times be very specific (as in physics, with Maxwell’s equations, relativity theory) but typically have some looseness to them (a biological model of how a particular drug works, a political science model of changes in public […]

Postdoc opportunity on Bayesian prediction for human-computer interfaces! In Stuttgart!

Paul “brms” Buerkner writes: At the Cluster of Excellence SimTech in Stuttgart, Germany, we are currently looking for a fully funded PostDoc (2 years) to work on Bayesian Intent Prediction for Human-Machine Collaboration, among others supervised by me (Paul-Christian Bürkner). The goal of this specific project is to contribute to the development of a new […]

The Xbox before its time: Using the famous 1936 Literary Digest survey as a positive example of statistical adjustment rather than a negative example of non-probability sampling

In this article from 2017, Sharon Lohr and J. Michael Brick write: The Literary Digest poll of 1936 is a byword for bad survey research. Textbooks have long used it as a prime example of how sampling goes bad . . . The story of the 1936 poll is well known. Ten million ballots were […]

“Using Benford’s Law to Detect Bitcoin Manipulation”

Economist Gary Smith sends along this post with the above title and the subtitle, “Market prices are not invariably equal to intrinsic values.” Here’s Smith: For a while, there was a popular belief among finance professors that the stock market is “efficient” in the sense that stock prices are always correct — the prices that […]

StanConnect 2021 is happening this summer/fall! Topics are Simulation Based Calibration, Ecology, Biomedical, and Cognitive Science and Neuroscience.

Arman Oganisian writes: The Stan Governing Body is excited to announce this year’s StanConnect 2021 lineup! For those who haven’t yet heard, StanConnect is a series of virtual sessions/mini-symposia held throughout the latter half of this year. Each session hosts research talks (and more) on Bayesian inference via Stan in a different field/topic areas. StanConnect […]

That $9 trillion bill on the sidewalk: Why don’t third-world countries borrow to get vaccines?

Gaurav Sood quotes this from June: “To get roughly 70% of the planet’s population inoculated by April, the IMF calculates, would cost just $50bn. The cumulative economic benefit by 2025, in terms of increased global output, would be $9trn, to say nothing of the many lives that would be saved.” https://www.economist.com/leaders/2021/06/09/the-west-is-passing-up-the-opportunity-of-the-century Gaurav continues: The Economist […]

Can statistical software do better at giving warnings when you apply a method when maybe you shouldn’t?

Gaurav Sood writes: There are legions of permutation-based methods which permute the value of a feature to determine whether the variable should be added (e.g., Boruta Algorithm) or its importance. I couldn’t reason for myself why that is superior to just dropping the feature and checking how much worse the fit is or what have […]

Thoughts on “The American Statistical Association President’s Task Force Statement on Statistical Significance and Replicability”

Megan Higgs writes: The statement . . . describes establishment of the task force to “address concerns that a 2019 editorial in The American Statistician (an ASA journal) might be mistakenly interpreted as official ASA policy. (The 2019 editorial recommended eliminating the use of ‘p

On fatally-flawed, valueless papers that journals refuse to retract

Commenter Carlos pointed us to this story (update here) of some scientists—Florin Moldoveanu, Richard Gill, and five others—all of whom seem to know what they’re talking about and who are indignant that the famous Royal Society of London published a paper that’s complete B.S. and then refused to retract it when the error was pointed […]

Impressive visualizations of social mobility

An anonymous tipster points to this news article by Emily Badger, Claire Cain Miller, Adam Pearce, and Kevin Quealy featuring an amazing set of static and dynamic graphs.

“The real thing, like the Perseverance mission, is slow, difficult and expensive, but far cooler than the make-believe alternative.”

Good point by Palko. He’s talking about the Mars rover: There’s a huge disconnect in our discussion of manned space travel. We’ve grown accustomed to vague promises about Martian cities just around the corner, but in the real world, our best engineering minds have never landed anything larger than a car on Mars and this […]

She sent a letter pointing out problems with a published article, the reviewers agreed that her comments were valid, but the journal didn’t publish her letter because “the policy among editors is not to accept comments.”

The journal in question is called The Economic Journal. To add insult to injury, the editor wrote the following when announcing they wouldn’t publish the letter: My [the editor’s] assessment is that this paper is a better fit for a field journal in education. OK, let me get this straight. The original paper, which was […]

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