The History of Statistics in the 20th Century

调查员将从事关于二十世纪上半叶统计历史的研究。 这项研究将通过卡尔和埃贡皮尔逊，罗纳德费舍尔，威廉S。Gosset，Jerzy Neyman，Harold Hotelling，Harold Jeffreys，Jimmie Savage，and Abraham Wald. 重点将放在概念的发展和理论发展和应用在物理，社会和生物科学的相互作用。 特别是要追求的线路包括参数估计的发展，出现的数理统计作为一个专业化，关系的奈曼皮尔逊理论和费雪统计，关系的理论博弈和统计决策理论，发展的多元分析，现代贝叶斯统计，和分类数据分析。 这项研究将基于对已发表和未发表的原始资料的详细研究。 预计这项研究将产生更多的现代统计方法的历史发展的知识，从而更深入地了解这些方法的属性和局限性。 例如，通过研究多变量分析从物理科学到社会科学的传播，其中使用相同的数值算法，但具有完全不同的概念框架，将有助于了解当前关于此类应用程序是否合适的辩论，包括使用回归来评估招聘中的歧视证据，评估教育计划以及估计人口普查漏报。 通过理解统计在过去的公共政策问题中的作用，包括禁酒、移民和军事事务，我们将能够更好地评估现代政策问题的统计组成部分。

The investigator will undertake research on the history of statistics during the first half of the twentieth century. The research will examine major lines of statistical development through the works of Karl and Egon Pearson, Ronald Fisher, William S. Gosset, Jerzy Neyman, Harold Hotelling, Harold Jeffreys, Jimmie Savage, and Abraham Wald. The emphasis will be on the development of concept and on the interplay of theoretical development and applications in the physical, social, and biological sciences. Particular lines to be pursued include the development of parametric estimation, the emergence of mathematical statistics as a specialization, the relationship of Neyman-Pearson theory and Fisherian statistics, the relationship of the theory of games and statistical decision theory, the development of multivariate analysis, modern Bayesian statistics, and categorical data analysis. The research will be based upon detailed study of original sources, both published and unpublished. It is expected that the research will yield a greater knowledge of the historical development of modern statistical methods, and thereby a deeper understanding of the properties and limitations of these methods. For example, by studying the spread of multivariate analysis from physical science to social science, where the same numerical algorithms are employed but with an entirely different conceptual framework, some light will be shed on the current debates on the appropriateness of such applications, including the use of regression to assess evidence of discrimination in hiring, the evaluation of educational programs, and the estimation of census undercount. By understanding the role of statistics in past questions of public policy, including temperance, immigration, and military affairs, we will be better able to assess the statistical component of modern policy questions.