What are the Most Important Statistical Ideas of the Past 50 Years?

We review the most important statistical ideas of the past half century, which we categorize as: counterfactual causal inference, bootstrapping and simulation-based inference, overparameterized models and regularization, Bayesian multilevel models, generic computation algorithms, adaptive decision analysis, robust inference, and exploratory data analysis. We discuss key contributions in these subfields, how they relate to modern computing and big data, and how they might be developed and extended in future decades. The goal of this article is to provoke thought and discussion regarding the larger themes of research in statistics and data science.

Automated summary

This article reviews the most important statistical ideas of the past half century, including counterfactual causal inference, bootstrapping, Bayesian multilevel models, etc. It discusses how these ideas relate to modern computing and big data, as well as potential future developments.