This text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.

One of Ian Hacking's earliest publications, this book showcases his early ideas on the central concepts and questions surrounding statistical reasoning. He explores the basic principles of statistical reasoning and tests them, both at a philosophical level and in terms of their practical consequences for statisticians. Presented in a fresh twenty-first-century series livery, and including a specially commissioned preface written by Jan-Willem Romeijn, illuminating its enduring importance and relevance to philosophical enquiry, Hacking's influential and original work has been revived for (...) a new generation of readers. (shrink)

David Annis is professor of philosophy at Ball State University. In this essay, Annis offers an alternative to the foundationalist-coherent controversy: "contextualism." This theory rejects both the idea of intrinsically basic beliefs in the foundational sense and the thesis that coherence is sufficient for justification. he argues that justification is relative to the varying norms of social practices.

This article reviews and develops an epistemological tradition in philosophy of science, called convergentism, which holds that inference methods should be assessed in terms of their abilities to converge to the truth. This tradition is compared with three competing ones: (1) explanationism, which holds that theory choice should be guided by a theory's overall balance of explanatory virtues, such as simplicity and fit with data; (2) instrumentalism, according to which scientific inference should be driven by the goal of obtaining useful (...) models, rather than true theories; (3) Bayesianism, which features a shift of focus from all-or-nothing beliefs to degrees of belief. (shrink)

This article explores the intersection of philosophy and statistics by examining the philosophical assumptions underlying modern mathematical statistics from ontological and epistemological perspectives. Statistics holds interest for philosophers engaged with the problem of induction, as its mathematical apparatus serves as models for philosophical ideas. For instance, the much-discussed concepts of the uniformity of nature and natural kinds correspond to probability models and statistical models, which are fundamental to various statistical methods. Similarly, Dennett’s concept of a real pattern echoes the spirit (...) of various information criteria (such as AIC) used to determine the optimal level of complexity for maximizing a model’s predictive ability. Furthermore, the recent developments in machine learning models, such as deep learning, imply that these machines possess their own 'ontology,' which is potentially more complex and efficient at understanding the world than ours. This leads to a Quinean problem of radical translation between human and machine ontologies. We suggest that this issue is key to the successful application of AI technologies in our society. The other focus of this article is epistemology, where Bayesian and classical statistics are compared to internalist and externalist epistemologies, respectively. This comparison elucidates how and in what sense the statistical methods adopted in each camp are considered to justify scientific hypotheses and also sheds light on their epistemic problems. We conclude with a plea for more research and interdisciplinary dialogues between statistics and the philosophy of various traditions. (shrink)

When I take a sip from the coffee in my cup, I can taste that it is sweet. When I hold the cup with my hands, I can feel that it is hot. Why does the experience of feeling that the cup is hot give me justification for believing that the cup is hot?And why does the experience of tasting that the coffee is sweet give me justification for believing that the coffee is sweet?In general terms: Why is it that (...) a sense experience that P is a source of justification—a reason—for believing that P? Call this the Question. I will discuss various answers to the Question, and defend the one I myself favor. (shrink)

When thinking about the debate between frequentists and Bayesians on scientific methodology, it is unproductive and misleading to choose from two dichotomized, oversimplified positions. A spectrum is developed here to make explicit some remarkable options, organized by two dimensions---or two questions. First, ask what kinds of probabilities exist. Only frequencies? Or only degrees of belief? Or both? Then ask what standards should be used to assess inference procedures. Only frequentist standards? Or just Bayesian ones? Or both? Or something else? In (...) combination, there are at least five positions that have been seriously defended in the history and philosophy of statistics. (shrink)

Annotation In this book Mr. Mackie selects for critical discussion six related topic which are prominent in John Locke's Essay concerning Human Understanding: ...