Gelman Andrew, O'Rourke Keith
Department of Statistics Columbia University, New York, NY 10027, USA.
Department of Political Science, Columbia University, New York, NY 10027, USA.
Entropy (Basel). 2024 Jul 30;26(8):652. doi: 10.3390/e26080652.
Amalgamation of evidence in statistics is conducted in several ways. Within a study, multiple observations are combined by averaging, or as factors in a likelihood or prediction algorithm. In multilevel modeling or Bayesian analysis, population or prior information is combined with data using the weighted averaging derived from probability modeling. In a scientific research project, inferences from data analysis are interpreted in light of mechanistic models and substantive theories. Within a scholarly or applied research community, data and conclusions from separate laboratories are amalgamated through a series of steps, including peer review, meta-analysis, review articles, and replication studies. These issues have been discussed for many years in the philosophy of science and statistics, gaining attention in recent decades first with the renewed popularity of Bayesian inference and then with concerns about the replication crisis in science. In this article, we review the amalgamation of statistical evidence from different perspectives, connecting the foundations of statistics to the social processes of validation, criticism, and consensus building.
统计学中证据的合并有多种方式。在一项研究中，多个观测值通过求平均值进行合并，或者作为似然或预测算法中的因素。在多层建模或贝叶斯分析中，总体信息或先验信息与数据通过概率建模得出的加权平均进行合并。在一个科研项目中，数据分析得出的推论会根据机理模型和实质性理论进行解释。在一个学术或应用研究群体中，来自不同实验室的数据和结论通过一系列步骤进行合并，包括同行评审、荟萃分析、综述文章和重复研究。这些问题在科学哲学和统计学领域已经讨论多年，近几十年来首先随着贝叶斯推理的再度流行受到关注，随后又因对科学中的重复危机的担忧而受到关注。在本文中，我们从不同角度回顾统计证据的合并，将统计学基础与验证、批评和共识形成的社会过程联系起来。
