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.

翻译：我们审视了过去半个世纪最重要的统计理念,我们将这些理念归类为:反事实因果推断、靴子穿梭和模拟推断、过度分解模型和正规化、贝叶斯多层次模型、通用计算算法、适应性决策分析、稳健推论和探索性数据分析。我们讨论了这些子领域的主要贡献,这些贡献与现代计算和大数据的关系,以及如何在未来几十年中发展和扩大这些贡献。本文章的目的是激发对统计和数据科学研究的更大主题的思考和讨论。