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Archive of posts filed under the Decision Theory category.

On deck this week

Mon: An inundation of significance tests Tues: Stock, flow, and two smoking regressions Wed: What’s the worst joke you’ve ever heard? Thurs: Cracked.com > Huffington Post, Wall Street Journal, New York Times Fri: Measurement is part of design Sat: “17 Baby Names You Didn’t Know Were Totally Made Up” Sun: What to do to train […]

Creativity is the ability to see relationships where none exist

Brent Goldfarb and Andrew King, in a paper to appear in the journal Strategic Management, write: In a recent issue of this journal, Bettis (2012) reports a conversation with a graduate student who forthrightly announced that he had been trained by faculty to “search for asterisks”. The student explained that he sifted through large databases […]

Can talk therapy halve the rate of cancer recurrence? How to think about the statistical significance of this finding? Is it just another example of the garden of forking paths?

James Coyne (who we last encountered in the sad story of Ellen Langer) writes: I’m writing to you now about another matter about which I hope you will offer an opinion. Here is a critique of a study, as well as the original study that claimed to find an effect of group psychotherapy on time […]

Bayesian inference: The advantages and the risks

This came up in an email exchange regarding a plan to come up with and evaluate Bayesian prediction algorithms for a medical application: I would not refer to the existing prediction algorithm as frequentist. Frequentist refers to the evaluation of statistical procedures but it doesn’t really say where the estimate or prediction comes from. Rather, […]

New Alan Turing preprint on Arxiv!

Dan Kahan writes: I know you are on 30-day delay, but since the blog version of you will be talking about Bayesian inference in couple of hours, you might like to look at paper by Turing, who is on 70-yr delay thanks to British declassification system, who addresses the utility of using likelihood ratios for […]

On deck this week

Mon: Bob Carpenter’s favorite books on GUI design and programming Tues: Bayesian inference: The advantages and the risks Wed: Objects of the class “Foghorn Leghorn” Thurs: “Physical Models of Living Systems” Fri: Creativity is the ability to see relationships where none exist Sat: Kaiser’s beef Sun: Chess + statistics + plagiarism, again!

My talk at MIT this Thursday

When I was a student at MIT, there was no statistics department. I took a statistics course from Stephan Morgenthaler and liked it. (I’d already taken probability and stochastic processes back at the University of Maryland; my instructor in the latter class was Prof. Grace Yang, who was super-nice. I couldn’t follow half of what […]

There’s No Such Thing As Unbiased Estimation. And It’s a Good Thing, Too.

Following our recent post on econometricians’ traditional privileging of unbiased estimates, there were a bunch of comments echoing the challenge of teaching this topic, as students as well as practitioners often seem to want the comfort of an absolute standard such as best linear unbiased estimate or whatever. Commenters also discussed the tradeoff between bias […]

On deck this week

Mon: There’s No Such Thing As Unbiased Estimation. And It’s a Good Thing, Too. Tues: There’s something about humans Wed: Humility needed in decision-making Thurs: The connection between varying treatment effects and the well-known optimism of published research findings Fri: I actually think this infographic is ok Sat: Apology to George A. Romero Sun: “Do […]

Collaborative filtering, hierarchical modeling, and . . . speed dating

Jonah Sinick posted a few things on the famous speed-dating dataset and writes: The main element that I seem to have been missing is principal component analysis of the different rating types. The basic situation is that the first PC is something that people are roughly equally responsive to, while people vary a lot with […]