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Archive of posts filed under the Decision Theory category.

“Why this gun control study might be too good to be true”

Jeff Lax points us to this news article by Carolyn Johnson discussing a research paper, “Firearm legislation and firearm mortality in the USA: a cross-sectional, state-level study,” by Bindu Kalesan, Matthew Mobily, Olivia Keiser, Jeffrey Fagan, and Sandro Galea, that just appeared in the medical journal The Lancet. Here are the findings from Kalesan et […]

The problems with p-values are not just with p-values: My comments on the recent ASA statement

The American Statistical Association just released a committee report on the use of p-values. I was one of the members of the committee but I did not write the report. We were also given the opportunity to add our comments. Here’s what I sent:

On deck this week

Mon: Smiley faces were never seen Tues: Bayesian inference for network links Wed: Some people are so easy to contact and some people aren’t. Thurs: Good advice can do you bad Fri: Statistics is like basketball, or knitting Sat: 0.05 is a joke Sun: The unbelievable reason that Jennifer Lawrence is using Waic and cross-validation […]

No, this post is not 30 days early: Psychological Science backs away from null hypothesis significance testing

A few people pointed me to this editorial by D. Stephen Lindsay, the new editor of Psychological Science, a journal that in recent years has been notorious for publishing (and, even more notoriously, promoting) click-bait unreplicable dead-on-arrival noise-mining tea-leaf-reading research papers. It was getting so bad for awhile that they’d be publishing multiple such studies […]

He’s looking for a textbook that explains Bayesian methods for non-parametric tests

Brandon Vaughan writes: I am in the market for a textbook that explains Bayesian methods for non-parametric tests. My experience with Bayesian statistics thus far comes from John Krushke’s Doing Bayesian Data Analysis, but this book excludes non-parametric statistics. I do see that your text, Bayesian Data Analysis 3e, covers non-parametric statistics, however, does it […]

Having trouble planning a replication? Here’s how the scientific publishing process gets in the way.

So, I decided to do a preregistered replication. Of one of my own projects. We made a four-step plan: (1) do a duplication, digging up our old code and our old data and checking that we could reproduce our published graphs; (2) clean our analysis in various ways and check that our results don’t change […]

On deck this week

Mon: Fitting the birthday model in Stan Tues: Having trouble planning a replication? Here’s how the scientific publishing process gets in the way. Wed: No, this post is not 30 days early: Psychological Science backs away from null hypothesis significance testing Thurs: At this point, even Tom Cruise is skeptical about claims of social priming. […]

Probability paradox may be killing thousands

Brian Kinghorn points to this news article by Christian Grothoff and J. M. Porup, “The NSA’s SKYNET program may be killing thousands of innocent people; ‘Ridiculously optimistic’ machine learning algorithm is ‘completely bullshit,’ says expert.” The article begins: In 2014, the former director of both the CIA and NSA proclaimed that “we kill people based […]

Fundamental difficulty of inference for a ratio when the denominator could be positive or negative

I happened to come across this post from 2011, which in turn is based on thoughts of mine from about 1993. It’s important and most of you probably haven’t seen it, so here it is again: Ratio estimates are common in statistics. In survey sampling, the ratio estimate is when you use y/x to estimate […]

On deck this week

We got some good stuff coming down the pike: Mon: Too big to fail: Why it’s unrealistic to expect erroneous scientific papers to be retracted Tues: An apology and a note on Stockholm Syndrome Wed: All that really important statistics stuff that isn’t in the statistics textbooks Thurs: Who falls for the education reform hype? […]

On deck this week

Mon: If Karl Popper edited the New York Times Tues: “What is Bayesian data analysis? Some examples”: My lecture at the New School this Wed noon Wed: You’ll never guess what David Cox wrote about the garden of forking paths! Thurs: Miller and Sanjurjo share 5 tips on how to hit the zeitgeist jackpot Fri: […]

On deck this week

Mon: Forking paths vs. six quick regression tips Tues: Primed to lose Wed: Point summary of posterior simulations? Thurs: In general, hypothesis testing is overrated and hypothesis generation is underrated, so it’s fine for these data to be collected with exploration in mind. Fri: “Priming Effects Replicate Just Fine, Thanks” Sat: Pooling is relative to […]

On deck this week

Mon: When does peer review make no damn sense? Tues: Stunning breakthrough: Using Stan to map cancer screening! Wed: Where the fat people at? Thurs: The Notorious N.H.S.T. presents: Mo P-values Mo Problems Fri: What’s the difference between randomness and uncertainty? Sat: You’ll never guess what I say when I have nothing to say Sun: […]

Postdoc opportunity with Sophia Rabe-Hesketh and me in Berkeley!

Sophia writes: Mark Wilson, Zach Pardos and I are looking for a postdoc to work with us on a range of projects related to educational assessment and statistical modeling, such as Bayesian modeling in Stan (joint with Andrew Gelman). See here for more details. We will accept applications until February 26. The position is for […]

The time-reversal heuristic—a new way to think about a published finding that is followed up by a large, preregistered replication (in context of Amy Cuddy’s claims about power pose)

[Note to busy readers: If you’re sick of power pose, there’s still something of general interest in this post; scroll down to the section on the time-reversal heuristic. I really like that idea.] Someone pointed me to this discussion on Facebook in which Amy Cuddy expresses displeasure with my recent criticism (with Kaiser Fung) of […]

On deck this week

Mon: Ted Versus Powerpose and the Moneygoround, Part One Tues: “Null hypothesis” = “A specific random number generator” Wed: “Why IT Fumbles Analytics Projects Thurs: Is a 60% risk reduction really no big deal? Fri: Placebo effect shocker: After reading this, you won’t know what to believe. Sat: TOP SECRET: Newly declassified documents on evaluating […]

My namesake doesn’t seem to understand the principles of decision analysis

It says “Never miss another deadline.” But if you really could never miss your deadlines, you’d just set your deadlines earlier, no? It’s statics vs. dynamics all over again. That said, this advice seems reasonable: The author has also developed a foolproof method of structuring your writing, so that you make effective use of your […]

On deck this week

Mon: My namesake doesn’t seem to understand the principles of decision analysis Tues: Middle-aged white death trends update: It’s all about women in the south Wed: My talk Fri 1pm at the University of Chicago Thurs: If you’re using Stata and you want to do Bayes, you should be using StataStan Fri: One quick tip […]

On deck this week

Mon: New course: Street-Fighting Math Tues: Paxil: What went wrong? Wed: Pro-PACE, anti-PACE Thurs: My namesake doesn’t seem to understand the principles of decision analysis Fri: Risk aversion is a two-way street Sat: A reanalysis of data from a Psychological Science paper Sun: The devil really is in the details; or, You’ll be able to […]

Read this to change your entire perspective on statistics: Why inversion of hypothesis tests is not a general procedure for creating uncertainty intervals

Dave Choi writes: A reviewer has pointed me something that you wrote in your blog on inverting test statistics. Specifically, the reviewer is interested in what can happen if the test can reject the entire assumed family of models, and has asked me to consider discussing whether it applies to a paper that I am […]