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“The Book of Why” by Pearl and Mackenzie

Judea Pearl and Dana Mackenzie sent me a copy of their new book, “The book of why: The new science of cause and effect.” There are some things I don’t like about their book, and I’ll get to that, but I want to start with a central point of theirs with which I agree strongly. […]

Did she really live 122 years?

Even more famous than “the Japanese dude who won the hot dog eating contest” is “the French lady who lived to be 122 years old.” But did she really? Paul Campos points us to this post, where he writes: Here’s a statistical series, laying out various points along the 100 longest known durations of a […]

The seminar speaker contest begins: Jim Thorpe (1) vs. John Oliver

As promised, we’ll be having one contest a day for our Ultimate Seminar Speaker contest, first going through the first round of our bracket, then going through round 2, etc., through to the finals. Here’s the bracket: And now we begin! The first matchup is Jim Thorpe, seeded #1 in the GOATs category, vs. John […]

On deck for the first half of 2019

OK, this is what we’ve got for you: “The Book of Why” by Pearl and Mackenzie Reproducibility and Stan MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about Becker on Bohm on the important role of stories in science This is one offer I can refuse How post-hoc power calculation is like a […]

Objective Bayes conference in June

Christian Robert points us to this Objective Bayes Methodology Conference in Warwick, England in June. I’m not a big fan of the term “objective Bayes” (see my paper with Christian Hennig, Beyond subjective and objective in statistics), but the conference itself looks interesting, and there are still a few weeks left for people to submit […]

Announcing the ultimate seminar speaker contest: 2019 edition!

Paul Davidson made the bracket for us (thanks, Paul!): Here’s the full list: Wits: Oscar Wilde (seeded 1 in group) Dorothy Parker (2) David Sedaris (3) Voltaire (4) Veronica Geng Albert Brooks Mel Brooks Monty Python Creative eaters: M. F. K. Fisher (1) Julia Child (2) Anthony Bourdain (3) Alice Waters (4) A. J. Liebling […]

“Dissolving the Fermi Paradox”

Jonathan Falk writes: A quick search seems to imply that you haven’t discussed the Fermi equation for a while. This looks to me to be in the realm of Miller and Sanjurjo: a simple probabilistic explanation sitting right under everyone’s nose. Comment? “This” is a article, Dissolving the Fermi Paradox, by Anders Sandberg, Eric Drexler […]

Back by popular demand . . . The Greatest Seminar Speaker contest!

Regular blog readers will remember our seminar speaker competition from a few years ago. Here was our bracket, back in 2015: And here were the 64 contestants: – Philosophers: Plato (seeded 1 in group) Alan Turing (seeded 2) Aristotle (3) Friedrich Nietzsche (4) Thomas Hobbes Jean-Jacques Rousseau Bertrand Russell Karl Popper – Religious Leaders: Mohandas […]

Robin Pemantle’s updated bag of tricks for math teaching!

Here it is! He’s got the following two documents: – Tips for Active Learning in the College Setting – Tips for Active Learning in Teacher Prep or in the K-12 Setting This is great stuff (see my earlier review here). Every mathematician and math teacher in the universe should read this. So, if any of […]

Published in 2018

R-squared for Bayesian regression models. {\em American Statistician}. (Andrew Gelman, Ben Goodrich, Jonah Gabry, and Aki Vehtari) Voter registration databases and MRP: Toward the use of large scale databases in public opinion research. {\em Political Analysis}. (Yair Ghitza and Andrew Gelman) Limitations of “Limitations of Bayesian leave-one-out cross-validation for model selection.” {\em Computational Brain and […]

What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis?

Someone writes: I would like to ask you for an advice regarding obtaining data for reanalysis purposes from an author who has multiple papers with statistical errors and doesn’t want to share the data. Recently, I reviewed a paper that included numbers that had some of the reported statistics that were mathematically impossible. As the […]

“Principles of posterior visualization”

What better way to start the new year than with a discussion of statistical graphics. Mikhail Shubin has this great post from a few years ago on Bayesian visualization. He lists the following principles: Principle 1: Uncertainty should be visualized Principle 2: Visualization of variability ≠ Visualization of uncertainty Principle 3: Equal probability = Equal […]

Authority figures in psychology spread more happy talk, still don’t get the point that much of the published, celebrated, and publicized work in their field is no good (Part 2)

Part 1 was here. And here’s Part 2. Jordan Anaya reports: Uli Schimmack posted this on facebook and twitter. I [Anaya] was annoyed to see that it mentions “a handful” of unreliable findings, and points the finger at fraud as the cause. But then I was shocked to see the 85% number for the Many […]

Combining apparently contradictory evidence

I want to write a more formal article about this, but in the meantime here’s a placeholder. The topic is the combination of apparently contradictory evidence. Let’s start with a simple example: you have some ratings on a 1-10 scale. These could be, for example, research proposals being rated by a funding committee, or, umm, […]

“Check yourself before you wreck yourself: Assessing discrete choice models through predictive simulations”

Timothy Brathwaite sends along this wonderfully-titled article (also here, and here’s the replication code), which begins: Typically, discrete choice modelers develop ever-more advanced models and estimation methods. Compared to the impressive progress in model development and estimation, model-checking techniques have lagged behind. Often, choice modelers use only crude methods to assess how well an estimated […]

Using multilevel modeling to improve analysis of multiple comparisons

Justin Chumbley writes: I have mused on drafting a simple paper inspired by your paper “Why we (usually) don’t have to worry about multiple comparisons”. The initial idea is simply to revisit frequentist “weak FWER” or “omnibus tests” (which assume the null everywhere), connecting it to a Bayesian perspective. To do this, I focus on […]

Back to the Wall

Jim Windle writes: Funny you should blog about Jaynes. Just a couple of days ago I was looking for something in his book’s References/Bibliography (it along with “Godel, Escher, Bach” and “Darwin’s Dangerous Idea” have bibliographies which I find not just useful but entertaining), and ran across something I wanted to send you but I […]

What is probability?

This came up in a discussion a few years ago, where people were arguing about the meaning of probability: is it long-run frequency, is it subjective belief, is it betting odds, etc? I wrote: Probability is a mathematical concept. I think Martha Smith’s analogy to points, lines, and arithmetic is a good one. Probabilities are […]

“Thus, a loss aversion principle is rendered superfluous to an account of the phenomena it was introduced to explain.”

What better day than Christmas, that day of gift-giving, to discuss “loss aversion,” the purported asymmetry in utility, whereby losses are systematically more painful than gains are pleasant? Loss aversion is a core principle of the heuristics and biases paradigm of psychology and behavioral economics. But it’s been controversial for a long time. For example, […]

June is applied regression exam month!

So. I just graded the final exams for our applied regression class. Lots of students made mistakes which gave me the feeling that I didn’t teach the material so well. So I thought it could help lots of people out there if I were to share the questions, solutions, and common errors. It was an […]