## Hey! Check out this short new introductory social science statistics textbook by Elena Llaudet and Kosuke Imai

Elena Llaudet points us to this new textbook for complete beginners. Here’s the table of contents: 1. Introduction [mostly on basic operations in R] 2. Estimating Causal Effects with Randomized Experiments [goes...

## Stop talking about “statistical significance and practical significance”

You’ve heard it a million times already: “statistical significance” is not the same as “practical significance.” The idea you’re supposed to have in mind, I think, is some effect size estimated at 0.003 with a standard error of 0.001. It’s statistically significant but not practically significant! I’m assuming here that these numbers have some interpretable scale, for example normalized based on total sales or average...

## “Unrepresentative big surveys significantly overestimated US vaccine uptake,” and the problem of generalizing from sample to population

First the story, then the background, then my final thoughts. 1. The story Valerie Bradley writes: Our (me, Shiro Kuriwaki, Michael Isakov, Dino Sejdinovic, Xiao-Li Meng, Seth Flaxman) recent article in Nature shows...

## Patterns on the complex floor: A fun little example of simulation-based experimentation in mathematics

Calling it a “fun little example” might sound disrespectful, but it’s not. Examples are not hard to come by, but “fun” and “little” are special. Just as it’s said that it can take a lot of work to write...

## Estimates of “false positive” rates in various scientific fields

Uli Schimmack writes: I am curious what you think about our recent attempts to estimate the false discovery risk (maximum rate under assumption of 100% power) based on estimates of a bias-corrected discovery rate? We applied this method to medicine (similar to Jager and Leek, 2014) and psychology (hand-coding). Results are very similar with an FDR estimate between 10 and 20 percent. Based on results we recommend an alpha of .01 to...

## Wilkinson’s contribution to interactive visualization

This is Jessica. Upon learning this morning that Lee Wilkinson passed away I also felt compelled to write something on the extent to which his work has influenced interactive visualization research. The Grammar of Graphics was an incredibly ambitious undertaking – Wilkinson set out to create a system that could produce any statistical graphic he’d ever seen, and that could deepen understanding of the meaning of graphics. The GoG...

## When confidence intervals include unreasonable values . . . When confidence intervals include only unreasonable values . . .

Robert Kaestner writes: Economists’ love affair with randomized controlled trials (RCTs) is growing stronger by the day. But what should we make of an RCT that produces a point estimate and confidence interval that largely includes values that most would consider implausible? The Goldin et al. article on effects of health insurance on mortality (QJE) provides a good example. Point estimate suggests that 6 months of extra insurance...

## Importance of understanding variation when considering how a treatment effect will scale

Art Owen writes: I saw the essay, “Nothing Scales,” by Jason Kerwin, which might be a good topic for one of your blog posts. Maybe a bunch of other people sent it to you already. He seems to think we just need more and better data and methods to get things to generalize/scale. It’s not clear to me that we’ll get enormously better data per subject on education or behavior. Maybe we will get better sets of subjects (more...

## “Have we been thinking about the pandemic wrong? The effect of population structure on transmission”

Philippe Lemoine writes: I [Lemoine] just published a blog post in which I explore what impact population structure might have on the transmission of an infectious disease such as COVID-19, which I thought might be of...

## A little correlation puzzle, leading to a discussion of the connections between intuition and brute force

Shane Frederick wrote: One of my favorite questions to ask people is: r(a,b) = x; r(b,c) = x; r(a,c) = 0. How big could x be? The answer feels unintuitive to me. And it is unintuitive to almost all, I’ll add. I took...

## Comparing bias and overfitting in learning from data across social psych and machine learning

This is Jessica. Not too long ago, I wrote about research on when claims in machine learning (ML)-oriented research are not reproducible, where I found it useful to try to contrast the nature of claims, and threats to their validity, in artificial intelligence and machine learning versus in a social science like psych where there’s been plenty of public discussion of ways authors can overclaim. Writing 20 years ago on the “two...

## Holland academic statistics horror story

X points us to this news article by Mark Reid and Susan Wichgers, which “reads like a murder mystery, the victim being the best stats department in the Netherlands.” It’s a horrible story involving what appears to be the intentional destruction of data—a true statistical crime. Speaking as a political scientist, I’m reminded of an earlier discussion of academic misconduct, where I wrote: Academic and corporate environments...

## Responding to Richard Morey on p-values and inference

Jonathan Falk points to this post by Richard Morey, who writes: I [Morey] am convinced that most experienced scientists and statisticians have internalized statistical insights that frequentist statistics attempts to formalize: how you can be fooled by randomness; how what we see can be the result of biasing mechanisms; the importance of understanding sampling distributions. In typical scientific practice, the “null hypothesis...

## When can a predictive model improve by anticipating behavioral reactions to its predictions?

This is Jessica. Most of my research involves data interfaces in some way or another, and recently I’ve felt pulled toward asking more theoretical questions about what effects interfaces can or should have in...