# What are the most important statistical ideas of the past 50 years?

arXiv: https://arxiv.org/abs/2012.00174 · HTML: https://mlsanity.com/paper/2012.00174/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Amortized Variational Inference for Simple Hierarchical Models](https://mlsanity.com/paper/2111.03144/) (2021)
- [Adaptive Label Smoothing for Out-of-Distribution Detection](https://mlsanity.com/paper/2410.06134/) (2024)
- [Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring](https://mlsanity.com/paper/2212.05866/) (2022)

## Abstract (arXiv)

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.