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Archive of posts filed under the Decision Theory category.

The Pandora Principle in statistics — and its malign converse, the ostrich

The Pandora Principle is that once you’ve considered a possible interaction or bias or confounder, you can’t un-think it. The malign converse is when people realize this and then design their studies to avoid putting themselves in a position where they have to consider some potentially important factor. For example, suppose you’re considering some policy […]

Wolfram on Golomb

I was checking out Stephen Wolfram’s blog and found this excellent obituary of Solomon Golomb, the mathematician who invented the maximum-length linear-feedback shift register sequence, characterized by Wolfram as “probably the single most-used mathematical algorithm idea in history.” But Golomb is probably more famous for inventing polyominoes. The whole thing’s a good read, and it […]

An improved ending for The Martian

In this post from a couple years ago I discussed the unsatisfying end of The Martian. At the time, I wrote: The ending is not terrible—at a technical level it’s somewhat satisfying (I’m not enough of a physicist to say more than that), but at the level of construction of a story arc, it didn’t […]

Died in the Wool

Garrett M. writes: I’m an analyst at an investment management firm. I read your blog daily to improve my understanding of statistics, as it’s central to the work I do. I had two (hopefully straightforward) questions related to time series analysis that I was hoping I could get your thoughts on: First, much of the […]

How to design future studies of systemic exercise intolerance disease (chronic fatigue syndrome)?

Someone named Ramsey writes on behalf of a self-managed support community of 100+ systemic exercise intolerance disease (SEID) patients. He read my recent article on the topic and had a question regarding the following excerpt: For conditions like S.E.I.D., then, the better approach may be to gather data from people suffering “in the wild,” combining […]

Hey—here are some tools in R and Stan to designing more effective clinical trials! How cool is that?

In statistical work, design and data analysis are often considered separately. Sometimes we do all sorts of modeling and planning in the design stage, only to analyze data using simple comparisons. Other times, we design our studies casually, even thoughtlessly, and then try to salvage what we can using elaborate data analyses. It would be […]

Classical statisticians as Unitarians

[cat picture] Christian Robert, Judith Rousseau, and I wrote: Several of the examples in [the book under review] represent solutions to problems that seem to us to be artificial or conventional tasks with no clear analogy to applied work. “They are artificial and are expressed in terms of a survey of 100 individuals expressing support […]

Statisticians and economists agree: We should learn from data by “generating and revising models, hypotheses, and data analyzed in response to surprising findings.” (That’s what Bayesian data analysis is all about.)

Kevin Lewis points us to this article by economist James Heckman and statistician Burton Singer, who write: All analysts approach data with preconceptions. The data never speak for themselves. Sometimes preconceptions are encoded in precise models. Sometimes they are just intuitions that analysts seek to confirm and solidify. A central question is how to revise […]

No, I’m not blocking you or deleting your comments!

Someone wrote in: I am worried you may have blocked me from commenting on your blog (because a couple of comments I made aren’t there). . . . Or maybe I failed to post correctly or maybe you just didn’t think my comments were interesting enough. . . . This comes up from time to […]

Bayesian, but not Bayesian enough

Will Moir writes: This short New York Times article on a study published in BMJ might be of interest to you and your blog community, both in terms of how the media reports science and also the use of bayesian vs frequentist statistics in the study itself. Here is the short summary from the news […]

Problems with the jargon “statistically significant” and “clinically significant”

Someone writes: After listening to your EconTalk episode a few weeks ago, I have a question about interpreting treatment effect magnitudes, effect sizes, SDs, etc. I studied Econ/Math undergrad and worked at a social science research institution in health policy as a research assistant, so I have a good amount of background. At the institution […]

Analyze all your comparisons. That’s better than looking at the max difference and trying to do a multiple comparisons correction.

[cat picture] The following email came in: I’m in a PhD program (poli sci) with a heavy emphasis on methods. One thing that my statistics courses emphasize, but that doesn’t get much attention in my poli sci courses, is the problem of simultaneous inferences. This strikes me as a problem. I am a bit unclear […]

On deck through the rest of the year (and a few to begin 2018)

Here they are. I love seeing all the titles lined up in one place; it’s like a big beautiful poem about statistics: After Peptidegate, a proposed new slogan for PPNAS. And, as a bonus, a fun little graphics project. “Developers Who Use Spaces Make More Money Than Those Who Use Tabs” Question about the secret […]

Ride a Crooked Mile

Joachim Krueger writes: As many of us rely (in part) on p values when trying to make sense of the data, I am sending a link to a paper Patrick Heck and I published in Frontiers in Psychology. The goal of this work is not to fan the flames of the already overheated debate, but […]

The Publicity Factory: How even serious research gets exaggerated by the process of scientific publication and media exposure

The starting point is that we’ve seen a lot of talk about frivolous science, headline-bait such as the study that said that married women are more likely to vote for Mitt Romney when ovulating, or the study that said that girl-named hurricanes are more deadly than boy-named hurricanes, and at this point some of these […]

How has my advice to psychology researchers changed since 2013?

Four years ago, in a post entitled, “How can statisticians help psychologists do their research better?”, I gave the following recommendations to researchers: – Analyze all your data. – Present all your comparisons. – Make your data public. And, for journal editors, I wrote, “if a paper is nothing special, you don’t have to publish […]

All the things we have to do that we don’t really need to do: The social cost of junk science

I’ve been thinking a lot about junk science lately. Some people have said it’s counterproductive or rude of me to keep talking about the same few examples (actually I think we have about 15 or so examples that come up again and again), so let me just speak generically about the sort of scientific claim […]

The Other Side of the Night

Don Green points us to this quantitative/qualitative meta-analysis he did with Betsy Levy Paluck and Seth Green. The paper begins: This paper evaluates the state of contact hypothesis research from a policy perspective. Building on Pettigrew and Tropp’s (2006) influential meta-analysis, we assemble all intergroup contact studies that feature random assignment and delayed outcome measures, […]

PCI Statistics: A preprint review peer community in statistics

X informs me of a new effort, “Peer community in . . .”, which describes itself as “a free recommendation process of published and unpublished scientific papers.” So far this exists in only one field, Evolutionary Biology. But this looks like a great idea and I expect it will soon exist in statistics, political science, […]

This company wants to hire people who can program in R or Python and do statistical modeling in Stan

Doug Puett writes: I am a 2012 QMSS [Columbia University Quantitative Methods in Social Sciences] grad who is currently trying to build a Data Science/Quantitative UX team, and was hoping for some advice. I am finding myself having a hard time finding people who are really interested in understanding people and who especially are excited […]