Seminal Ideas and Controversies in Statistics

The evolution of statistics is marked by breakthrough developments but also by lively controversies that have shaped the discipline. Appreciating these seminal ideas and controversies deepens understanding and hones critical thinking skills. This short course is based on a recently-published book: Little, R.J. (2025). Seminal Ideas and Controversies in Statistics. Chapman Hall/CRC.

The book discusses seminal papers on each topic, with supplementary papers to help elucidate the main ideas. The short course describes many of the key ideas, organized around seven major themes:

1. Estimating equations vs likelihood-based methods
2. Conditionality principle, strengths and weaknesses of frequentist
and Bayesian inference approaches, the Calibrated Bayes compromise
3. Fisher and Neyman's approaches to hypothesis testing, the recent ASA statement on P-Values and reactions
4. Empirical Bayes and random-effects models
5. Data science and exploratory data analysis
6. The role of randomization in surveys and experiments
7. Causal inference

The course is intended for individuals familiar with the main tools of statistics like multiple regression, repeated-measures analysis, basic properties of distributions, and key asymptotic approaches such as maximum likelihood, but are interested in the history and would like to deepen their understanding of key ideas.

History of Statistics Interest Group