CAREER: A Modern Philosophy for Classical Statistical Testing and Estimation

职业：经典统计测试和估计的现代哲学

• 批准号: 2042366
• 负责人: Samuel Fletcher
• 金额: $ 44.3万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2042366&HistoricalAwards=false
• 关键词: CAREER; Modern; Philosophy; Classical; Statistical; CAREER; Modern; Philosophy; Classical; Statistical

Classical statistical testing and estimation, based on the ideas of Fisher, Neyman, and Pearson, are ubiquitous in science. They continue to be central in psychology, medicine, epidemiology, pharmacology, agriculture, and environmental science, among others. Thus, they play an important role in those sciences' empirical successes. Yet they often fall short of scientists' ambitions to quantify how data positively support hypotheses and to express the variable confidence of estimates. To address such shortcomings, this project develops new conceptual foundations for statistical testing and estimation with quantitative implications. It modifies, extends, and strengthens those foundations by adapting ideas from 20th- and 21st-century epistemology, the philosophical study of the nature and conditions for evidence and knowledge. In doing so, it shows how to draw more nuanced scientific conclusions from data. This research also integrates with supported activities towards two educational objectives. First, this project promotes the integration of statistical concepts and methods into college philosophy curricula, especially critical reasoning, epistemology, and philosophy of science courses. It does so through two faculty summer institutes for college instructors. Second, this project begins to build a network of early-career scholars engaged in graduate-level research in the philosophy of statistics through two summer schools for early-stage graduate students. The main synergy between the educational and research objectives arises from the natural feedback between teaching and new research directions.The ideas that this research adapts from contemporary epistemology include the modal conditions for evidence and knowledge of adherence, sensitivity, and safety. From the viewpoint of probabilized reliabilist epistemology, Fisherian p-values are a probabilistic measure of adherence, while two novel quantitative post-data measures of evidence correspond to sensitivity and safety. These include a distinct post-data analogue of statistical power related to Mayo's severity concept. Data are evidence for a hypothesis to the extent they are sufficiently adherent, sensitive, and safe according to these measures. By contrast, traditional Fisherian significance testing only measures how adherent data are for a hypothesis, which is necessary but not sufficient for positive evidential support of that hypothesis. And Neyman-Pearson testing does not quantify the evidence that data provide for a hypothesis at all, but rather provides a decision procedure for accepting and rejecting hypotheses with specified rates error in the long run. The first part of this project develops the theoretical foundation for these ideas in the context of general statistical testing and estimation. The second part then implements these ideas computationally by modifying standard testing and estimation packages in the R programming language. This paves the way for the seamless adoption and application of these new nuanced measures of evidence in sciences that continue to use classical statistical testing and estimation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

基于Fisher，Neyman和Pearson的思想，经典的统计测试和估计在科学方面无处不在。他们继续在心理学，医学，流行病学，药理学，农业和环境科学等方面是中心。因此，它们在这些科学的经验成功中发挥了重要作用。然而，他们通常没有科学家的野心来量化数据如何积极支持假设并表达估计的可变置信度。为了解决此类缺点，该项目为具有定量含义的统计测试和估算开发了新的概念基础。它通过调整20世纪和21世纪认识论的思想（对证据和知识的性质和条件的哲学研究）来修改，扩展和增强这些基础。通过这样做，它显示了如何从数据中得出更细微的科学结论。这项研究还与支持两个教育目标的支持活动相结合。首先，该项目促进了将统计概念和方法整合到大学哲学课程中，尤其是批判推理，认识论和科学课程哲学。它通过两家大学教师的夏季学院的教师来做到这一点。其次，该项目开始建立一个通过两所暑期学校的早期研究生的统计学哲学研究的早期学者网络。教育目标与研究目标之间的主要协同作用是由教学和新研究方向之间的自然反馈引起的。该研究适应当代认识论的思想包括证据和依从性，敏感性和安全性知识的模态条件。从概率的可靠主义认识论的角度来看，费舍里亚人的p值是依从性的概率度量，而两种新型的定量证据测量证据测量与灵敏度和安全相对应。其中包括与梅奥严重性概念有关的统计能力的独特之后类似物。数据是一个假设的证据，其范围是根据这些措施足够坚固，敏感和安全的。相比之下，传统的Fisherian意义测试仅衡量假设的依从性数据是必要的，但对于该假设的积极证据支持是必要但不足以实现的。 Neyman-Pearson的测试并未量化数据完全提供假设的证据，而是提供了一种决策程序，从长远来看接受和拒绝具有指定率误差的假设。该项目的第一部分在一般统计测试和估计的背景下为这些思想建立了理论基础。然后，第二部分通过在R编程语言中修改标准测试和估计软件包来计算这些想法。这为这些新的细微差别证据衡量标准在继续使用经典统计测试和估计中的无缝采用和应用铺平了道路。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准通过评估来获得支持的。