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Thinking About Statistics: The Philosophical Foundations
Simply stated, this book bridges the gap between statistics and philosophy. It does this by delineating the conceptual cores of various statistical methodologies (Bayesian/frequentist statistics, model selection, machine learning, causal inference, etc.) and drawing out their philosophical implications. Portraying statistical inference as an epistemic endeavor to justify hypotheses about a probabilistic model of a given empirical problem, the book explains the role of ontological, semantic, and epistemological assumptions that make such inductive inference possible. From this perspective, various statistical methodologies are characterized by their epistemological nature: Bayesian statistics by internalist epistemology, classical statistics by externalist epistemology, model selection by pragmatist epistemology, and deep learning by virtue epistemology.
Another highlight of the book is its analysis of the ontological assumptions that underpin statistical reasoning, such as the uniformity of nature, natural kinds, real patterns, possible worlds, causal structures, etc. Moreover, recent developments in deep learning indicate that machines are carving out their own "ontology" (representations) from data, and better understanding this--a key objective of the book--is crucial for improving these machines' performance and intelligibility.
Key Features
Without assuming any prior knowledge of statistics, discusses philosophical aspects of traditional as well as cutting-edge statistical methodologies.
Draws parallels between various methods of statistics and philosophical epistemology, revealing previously ignored connections between the two disciplines.
Written for students, researchers, and professionals in a wide range of fields, including philosophy, biology, medicine, statistics and other social sciences, and business.
Originally published in Japanese with widespread success, has been translated into English by the author.
Another highlight of the book is its analysis of the ontological assumptions that underpin statistical reasoning, such as the uniformity of nature, natural kinds, real patterns, possible worlds, causal structures, etc. Moreover, recent developments in deep learning indicate that machines are carving out their own "ontology" (representations) from data, and better understanding this--a key objective of the book--is crucial for improving these machines' performance and intelligibility.
Key Features
Without assuming any prior knowledge of statistics, discusses philosophical aspects of traditional as well as cutting-edge statistical methodologies.
Draws parallels between various methods of statistics and philosophical epistemology, revealing previously ignored connections between the two disciplines.
Written for students, researchers, and professionals in a wide range of fields, including philosophy, biology, medicine, statistics and other social sciences, and business.
Originally published in Japanese with widespread success, has been translated into English by the author.
- GenresPhilosophyScience
194 pages, Kindle Edition
Published December 30, 2022
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Displaying 1 - 3 of 3 reviews
June 4, 2024
Giving five stars is clearly underestimating the value of this book, which is one of the best I had ever read. It's just fantastic.
June 2, 2024
The author made a huge effort to present the philosophical foundations of different parts of statistics in a clear way. In some parts he was more successful than others. Even though some parts were difficult, it served the purpose that I wanted.
April 23, 2023
I think this is the first systematic introduction to the philosophy of statistics (Hacking 1956 is not quite an introduction and it is old). This book is definitely worth reading and I enjoy reading it.
What I find inspiring in the book:
1) The author understands i.i.d. assumption in inferential statistics as an assumption about the uniformity of nature.
2) The book offers a novel perspective on the clash between Bayesian and classical statistics by appealing to the debate between internalist and externalist/reliabilist epistemology.
3) The book not only includes a chapter on causal inference, a relatively new area in statistics, but it introduces both the potential outcome framework and the causal modeling framework.
4) The author lays out a three-level ontology for contemporary statistics (Fig. 5.3 on p. 169): causality, probability, and data. Positivists like Pearson only see the first layer. Inferential statistics focuses on the inference from data to probability. Causal inference takes us from probability to causality.
What I find inspiring in the book:
1) The author understands i.i.d. assumption in inferential statistics as an assumption about the uniformity of nature.
2) The book offers a novel perspective on the clash between Bayesian and classical statistics by appealing to the debate between internalist and externalist/reliabilist epistemology.
3) The book not only includes a chapter on causal inference, a relatively new area in statistics, but it introduces both the potential outcome framework and the causal modeling framework.
4) The author lays out a three-level ontology for contemporary statistics (Fig. 5.3 on p. 169): causality, probability, and data. Positivists like Pearson only see the first layer. Inferential statistics focuses on the inference from data to probability. Causal inference takes us from probability to causality.
Displaying 1 - 3 of 3 reviews