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“If the horse race polls were all wrong about Trump, why should his approval rating polls be any better?”

A journalist forwarded the above question to me and asked what I thought. My reply is that the horse race polls were not all wrong about Trump. The polls had him at approx 48% of the two-party vote and he received 49%. The polls were wrong by a few percentage points in some key swing […]

Laurie Davies: time series decomposition of birthday data

On the cover of BDA3 is a Bayesian decomposition of the time series of birthdays in the U.S. over a 20-year period. We modeled the data as a sum of Gaussian processes and fit it using GPstuff. Occasionally we fit this model to new data; see for example this discussion of Friday the 13th and […]

We’re hiring! hiring! hiring! hiring!

[insert picture of adorable cat entwined with Stan logo] We’re hiring postdocs to do Bayesian inference. We’re hiring programmers for Stan. We’re hiring a project manager. How many people we hire depends on what gets funded. But we’re hiring a few people for sure. We want the best best people who love to collaborate, who […]

Powerpose update

I contacted Anna Dreber, one of the authors of the paper that failed to replicate power pose, and asked her about a particular question that came up regarding their replication study. One of the authors of the original power pose study wrote that the replication “varied methodologically in about a dozen ways — some of […]

To know the past, one must first know the future: The relevance of decision-based thinking to statistical analysis

We can break up any statistical problem into three steps: 1. Design and data collection. 2. Data analysis. 3. Decision making. It’s well known that step 1 typically requires some thought of steps 2 and 3: It is only when you have a sense of what you will do with your data, that you can […]

Frank Harrell statistics blog!

Frank Harrell, author of an influential book on regression modeling and currently both a biostatistics professor and a statistician at the Food and Drug Administration, has started a blog. He sums up “some of his personal philosophy of statistics” here: Statistics needs to be fully integrated into research; experimental design is all important Don’t be […]

No evidence of incumbency disadvantage?

Several years ago I learned that the incumbency advantage in India was negative! There, the politicians are so unpopular that when they run for reelection they’re actually at a disadvantage, on average, compared to fresh candidates. At least, that’s what I heard. But Andy Hall and Anthony Fowler just wrote a paper claiming that, no, […]

Problems with “incremental validity” or more generally in interpreting more than one regression coefficient at a time

Kevin Lewis points us to this interesting paper by Jacob Westfall and Tal Yarkoni entitled, “Statistically Controlling for Confounding Constructs Is Harder than You Think.” Westfall and Yarkoni write: A common goal of statistical analysis in the social sciences is to draw inferences about the relative contributions of different variables to some outcome variable. When […]

“A Conceptual Introduction to Hamiltonian Monte Carlo”

Michael Betancourt writes: Hamiltonian Monte Carlo has proven a remarkable empirical success, but only recently have we begun to develop a rigorous understanding of why it performs so well on difficult problems and how it is best applied in practice. Unfortunately, that understanding is con- fined within the mathematics of differential geometry which has limited […]

A small, underpowered treasure trove?

Benjamin Kirkup writes: As you sometimes comment on such things; I’m forwarding you a journal editorial (in a society journal) that presents “lessons learned” from an associated research study. What caught my attention was the comment on the “notorious” design, the lack of “significant” results, and the “interesting data on nonsignificant associations.” Apparently, the work […]

When do stories work, Process tracing, and Connections between qualitative and quantitative research

Jonathan Stray writes: I read your “when do stories work” paper (with Thomas Basbøll) with interest—as a journalist stories are of course central to my field. I wondered if you had encountered the “process tracing” literature in political science? It attempts to make sense of stories as “case studies” and there’s a nice logic of […]

I’ve said it before and I’ll say it again

Ryan Giordano, Tamara Broderick, and Michael Jordan write: In Bayesian analysis, the posterior follows from the data and a choice of a prior and a likelihood. One hopes that the posterior is robust to reasonable variation in the choice of prior, since this choice is made by the modeler and is often somewhat subjective. A […]

Problems with randomized controlled trials (or any bounded statistical analysis) and thinking more seriously about story time

In 2010, I wrote: As a statistician, I was trained to think of randomized experimentation as representing the gold standard of knowledge in the social sciences, and, despite having seen occasional arguments to the contrary, I still hold that view, expressed pithily by Box, Hunter, and Hunter (1978) that “To find out what happens when […]

Time Inc. stoops to the level of the American Society of Human Genetics and PPNAS?

Confirmation bias

Shravan Vasishth is unimpressed by this evidence that was given to support the claim that being bilingual postpones symptoms of dementia: My reaction: Seems like there could be some selection issues, no? Shravan: Also, low sample size, and confirming what she already believes. I would be more impressed if she found evidence against the bilingual […]

The Lure of Luxury

From the sister blog, a response to an article by psychologist Paul Bloom on why people own things they don’t really need: Paul Bloom argues that humans dig deep, look beyond the surface, and attend to the nonobvious in ways that add to our pleasure and appreciation of the world of objects. I [Susan] wholly […]

We fiddle while Rome burns: p-value edition

Raghu Parthasarathy presents a wonderfully clear example of disastrous p-value-based reasoning that he saw in a conference presentation. Here’s Raghu: Consider, for example, some tumorous cells that we can treat with drugs 1 and 2, either alone or in combination. We can make measurements of growth under our various drug treatment conditions. Suppose our measurements […]

“Which curve fitting model should I use?”

Oswaldo Melo writes: I have learned many of curve fitting models in the past, including their technical and mathematical details. Now I have been working on real-world problems and I face a great shortcoming: which method to use. As an example, I have to predict the demand of a product. I have a time series […]

Nooooooo, just make it stop, please!

Dan Kahan wrote: You should do a blog on this. I replied: I don’t like this article but I don’t really see the point in blogging on it. Why bother? Kahan: BECAUSE YOU REALLY NEVER HAVE EXPLAINED WHY. Gelman-Rubin criticque of BIC is *not* responsive; you have something in mind—tell us what, pls! Inquiring minds […]

When you add a predictor the model changes so it makes sense that the coefficients change too.

Shane Littrell writes: I’ve recently graduated with my Masters in Science in Research Psych but I’m currently trying to get better at my stats knowledge (in psychology, we tend to learn a dumbed down, “Stats for Dummies” version of things). I’ve been reading about “suppressor effects” in regression recently and it got me curious about […]