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Archive of posts filed under the Decision Theory category.

What does CNN have in common with Carmen Reinhart, Kenneth Rogoff, and Richard Tol: They all made foolish, embarrassing errors that would never have happened had they been using R Markdown

Rachel Cunliffe shares this delight: Had the CNN team used an integrated statistical analysis and display system such as R Markdown, nobody would’ve needed to type in the numbers by hand, and the above embarrassment never would’ve occurred. And CNN should be embarrassed about this: it’s much worse than a simple typo, as it indicates […]

On deck this week

Mon: More bad news for the buggy-whip manufacturers Tues: They know my email but they don’t know me Wed: What do you do to visualize uncertainty? Thurs: Sokal: “science is not merely a bag of clever tricks . . . Rather, the natural sciences are nothing more or less than one particular application — albeit […]

Six quotes from Kaiser Fung

You may think you have all of the data. You don’t. One of the biggest myth of Big Data is that data alone produce complete answers. Their “data” have done no arguing; it is the humans who are making this claim. Before getting into the methodological issues, one needs to ask the most basic question. […]

“It’s as if you went into a bathroom in a bar and saw a guy pissing on his shoes, and instead of thinking he has some problem with his aim, you suppose he has a positive utility for getting his shoes wet”

The notion of a geocentric universe has come under criticism from Copernican astronomy. . . . A couple months ago in a discussion of differences between econometrics and statistics, I alluded to the well-known fact that everyday uncertainty aversion can’t be explained by a declining marginal utility of money. What really bothers me—it’s been bothering […]

On deck this week

Mon: My talk with David Schiminovich this Wed noon: “The Birth of the Universe and the Fate of the Earth: One Trillion UV Photons Meet Stan” Tues: Suspiciously vague graph purporting to show “percentage of slaves or serfs in the world” Wed: “It’s as if you went into a bathroom in a bar and saw […]

Why isn’t replication required before publication in top journals?

Gabriel Power asks the above question, writing: I don’t recall seeing, on your blog or elsewhere, this question raised directly. Of course there is much talk about the importance of replication, mostly by statisticians, and economists are grudgingly following suit with top journals requiring datasets and code. But why not make it a simple requirement? […]

Questions about “Too Good to Be True”

Greg Won writes: I manage a team tasked with, among other things, analyzing data on Air Traffic operations to identify factors that may be associated with elevated risk. I think its fair to characterize our work as “data mining” (e.g., using rule induction, Bayesian, and statistical methods). One of my colleagues sent me a link […]

On deck this week

Mon: Bad Statistics: Ignore or Call Out? Tues: Questions about “Too Good to Be True” Wed: I disagree with Alan Turing and Daniel Kahneman regarding the strength of statistical evidence Thurs: Why isn’t replication required before publication in top journals? Fri: Confirmationist and falsificationist paradigms of science Sat: How does inference for next year’s data […]

On deck this month

Bad Statistics: Ignore or Call Out? Questions about “Too Good to Be True” I disagree with Alan Turing and Daniel Kahneman regarding the strength of statistical evidence Why isn’t replication required before publication in top journals? Confirmationist and falsificationist paradigms of science How does inference for next year’s data differ from inference for unobserved data […]

Avoiding model selection in Bayesian social research

One of my favorites, from 1995. Don Rubin and I argue with Adrian Raftery. Here’s how we begin: Raftery’s paper addresses two important problems in the statistical analysis of social science data: (1) choosing an appropriate model when so much data are available that standard P-values reject all parsimonious models; and (2) making estimates and […]