Best read for Passover this year

Nathaniel Haines@natehaines.bsky.social · 6mo ago

Part 2 of my shrinkage estimator series is out! Part 1 covered the univariate case, but now we dive into multivariate shrinkage 🤓 We cover Spearman's classic correlation disattenuation formula, multivariate James-Stein estimators, and hierarchical methods too haines-lab.com/post/how-to-...

New blog just dropped! This one is all about estimators—we cover James-Stein, classical test theory, empirical Bayes, penalized regression, and hierarchical models, showing how they all can be used to do a better job than sample stats alone 🤓 haines-lab.com/post/how-to-...

How to Estimate a Mean, and What It Means for Science | Computational Psychology

Introduction If I asked you to estimate 30 means, you would probably compute 30 sample means. And you would be provably wrong (well.. maybe not wrong, but at least provably inefficient).

haines-lab.com

1/9 New blog is live! This is part 2 of a series—last time we looked at the Dunning-Kruger effect, now we are digging in to Implicit vs Explicit attitudes and the Implicit Association Test. To start, of course we need a good meme... haines-lab.com/post/part-2-...

Our paper is finally out in Cognition! 🎉 We introduce the "CLIP task"—a computerized paradigm for measuring numerical bias in adults: when number and physical size both matter, do you spontaneously rely more on numbers or on physical size?

New preprint with @rogierk.bsky.social @paulbuerkner.com - we introduce "relative measurement uncertainty" - a reliability estimation method that's applicable across a broad class of Bayesian measurement models (e.g., generative-, computational- and item response theory-models osf.io/h54k8

New blog post! Ever wonder what geom_histogram is actually doing? How about geom_boxplot? In celebration of the release of #ggplot2 4.0.0 (ggplot8?), I explore the relationships between the “geoms” and “stats” offered by the core {ggplot2} functions. #rstats

Exploring {ggplot2}’s Geoms and Stats – Stat’s What It’s All About

blog.msbstats.info

New #rstats blog up! solomonkurz.netlify.app/blog/2025-07... This is the first in a new series discussing causal inference with experimental data using multilevel models. My basic case is g-computation is the way to go.

Within-person factorial experiments, log(normal) reaction-time data | A. Solomon Kurz

Causal inference with the GLMM, Part 1

solomonkurz.netlify.app

1/3 New post up! 📝 I took the workhorse 🔧 of binary modeling—logistic regression—and gave it a Bayesian tune-up using a Kaggle SMS-spam dataset.

1/6 Hello Bluesky! 👋 Excited to join this community and share my new blog. First post: Using Bayesian hierarchical models to rescue "unreliable" cognitive tasks, with the dot-probe task as my case study. cogpsychreserve.netlify.app/posts/dotpro...