Introduction to recent issues in philosophy of statistics: evidence, testing, and applications

Abstract

Statistics play an essential role in an extremely wide range of human reasoning. From theorizing in the physical and social sciences to determining evidential standards in legal contexts, statistical methods are ubiquitous, and thus various questions about their application inevitably arise. As tools for making inferences that go beyond a given set of data, they are inherently a means of reasoning ampliatively, and so it is unsurprising that philosophers interested in the notions of evidence and inductive inference have been concerned to utilize statistical frameworks to further our understanding of these topics. However, the field of statistics has long been the subject of heated philosophical controversy. Given that a central goal for philosophers of science is to help resolve problems about evidence and inference in scientific practice, it is important that they be involved in current debates in statistics and data science. The purpose of this topical collection is to promote such philosophical interaction. We present a cross-section of these subjects, written by scholars from a variety of fields in order to explore issues in philosophy of statistics from different perspectives.The articles in this collection can be divided into roughly two categories. The first group contain articles by Mayo and Hand (2022), Radzvilas et al. (2021), Rubin (2021), and Spanos (2021, and are concerned mainly with foundational issues in philosophy of statistics. In particular, the authors address questions on the procedure of statistical significance testing and its accompanying concepts of p-values and significance thresholds, Bayesian versus frequentist ("classical") statistics, and Ber-