Explores the history of data science through early 20th-century rat diet experiments, drawing parallels to modern statistical methods.
A critique of statistical inference's reliance on p-values and combinatorics, arguing it obscures real-world causality and individual context.
A data-driven analysis of LLM performance on a simple retrieval task, highlighting the need for evidence-based AI testing.
Explains blocking, covariate adjustment, and optimal design to improve statistical power in online experiments, with a Python implementation.
Announcing the 2022 Ihaka Lectures, featuring online talks by Emi Tanaka, Luke Tierney, and Wes McKinney on R, data science tools, and experimental design.
A technical blog post discussing Bayesian priors, sparsity in high-dimensional models, and scale-mixture of normal priors for statistical computation.
