Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity an
Philosophers of science and scientific practitioners are challenged to reevaluate the assumptions of their own theories - philosophical or methodological.
Practitioners may better appreciate the foundational issues around which their questions revolve and thereby become better 'applied philosophers'. Conversely, new avenues emerge for finally solving recalcitrant philosophical problems of induction, explanation and theory testing. This book offers a welcome bridge between current philosophy of science and scientific practice, providing the reader with new insights on important topics such as statistical inference, reliability, theory testing, causal modeling, and the relation between theory and experiment.
The book will have a wide and enthusiastic readership among philosophers and scientists. The editors suppose that venerable philosophical problems surrounding induction, scientific inference, and objectivity can be solved. The essays in the book give support to that perspective. They also show that pressing practical problems of scientific inference and testing gain marked benefit from careful attention to philosophers' accounts of what makes for evidence, rationality, and objectivity. In this superb volume Mayo and Spanos face their critics and show that error-probabilism is able to solve most theoretical puzzles of statistical testing.
If some issue in the field of inductive inference is bothering you, you will probably find an answer in this book. Their important volume deserves a broad readership. This book begins with a fine introduction to Mayo's error-statistical approach that makes the book a useful teaching tool. But then it carries forward the discussion of this approach with challenging papers from Glymour, Laudan, Achinstein, Worrall, and others. It contains original and stimulating essays by leading figures from both philosophy and statistics on notions of evidence and testing; on how these interact with ideas about causation, explanation, and scientific rationality; and much more besides.
The volume also features detailed and illuminating exchanges between the contributors. A must-read for anyone with an interest in these topics. Philosophy of methodological practice Deborah Mayo 2.
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Critical rationalism, explanation and severe tests Alan Musgrave 7. Towards progressive critical rationalism: Error, tests and theory-confirmation John Worrall 9. Has Worrall saved his theory on ad hoc saves in a non ad hoc manner? Mill's sins, or Mayo's errors? Sins of the Bayesian epistemologist: Frequentist statistics as a theory of inductive inference Deborah Mayo and David Cox Mayo - - In Deborah G.
Review of Deborah G. Mayo, Aris Spanos Eds. Error and the Law: Exchanges with Larry Laudan. Exchanges with Alan Chalmers. Frequentist Statistics as a Theory of Inductive Inference.
Ad Hoc Save of a Theory of Adhocness? Exchanges with John Worrall.
Objectivity and Conditionality in Frequentist Inference. Toward Progressive Critical Rationalism: Exchanges with Alan Musgrave. Sins of the Epistemic Probabilist: Exchanges with Peter Achinstein. Added to PP index Total downloads 72 86, of 2,, Recent downloads 6 months 4 , of 2,, How can I increase my downloads?
Mayo's reply largely focuses on Achinstein's empirical probabilism.
She quite reasonably wants the details: How does Achinstein arrive at his objective epistemic posterior probabilities? And how do the probabilities ensure reliable inference? This reply to Achinstein encapsulates Mayo's challenge to Bayesian philosophers of science. Topics discussed in the second half of the volume are more varied than the first. Chapters consider the application of the error-statistical philosophy to econometrics and legal epistemology, the intersection of error-statistical philosophy and causal graphical models, and the foundations of frequentist inference.
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For teaching purposes Mayo and Spanos suggest selecting topics according to the focus of the course. Spanos' chapter on economic modelling Chapter 6 is a good introduction to the philosophy of economics and empirical modelling. In addition, he provides a solid argument for the benefits of the error-statistical approach in what has historically been a theory-dominated field.
Larry Laudan Chapter 9 argues for a closer attention to an epistemology of error in law. Laudan maps how burden of proof and standards of evidence shift for affirmative defences in criminal law. It certainly appears that something goes awry in the standard legal approach to affirmative defences. To the extent this is correct, error-statistical philosophy has something to offer.
2010.07.29
In response to Laudan, Mayo sketches some of the ways that standards of evidence could be clarified in legal contexts. Clark Glymour Chapter 8 explores the links between explanation, testing and truth. Specifically, Glymour claims that causal modelling provides an opportunity to severely test causal explanations.
Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science connecting problems of traditional philosophy of science to problems of inference in statistical and empirical modelling practice. Error and Inference: Recent Exchanges on Experimental. Reasoning, Reliability, and the Objectivity and Rationality of Science. Edited By Deborah G. Mayo and.
He provides details on procedures for selecting causal models from the set of possible causal relations and describes tests that can be conducted to examine the extent to which the assumptions underpinning the causal graphical model can be verified. Glymour's chapter is challenging, but rewards close attention. Work on causal modelling provides an attractive avenue of research for those areas of science that rely heavily on hypothesis testing and estimation but continue to eschew causal interpretations.
Chapter 7 will be of particular interest to philosophers of statistics. Part I and II elucidate a philosophy of frequentist inference for the error-statistical approach. Cox and Mayo argue for an interpretation of error-statistical tests along the lines of Fisherian p values as opposed to Neyman-Pearson long-run error rates.
This permits a post-data inductive interpretation of error-statistical tests and avoids some of the counterexamples that arise against the strictly pre-data perspective of Neyman-Pearson hypothesis tests. The main contribution of this chapter is that it provides a clear, accessible and comprehensive account of the approach to frequentist inference from the error-statistical perspective. Given the central role played by error-statistical tests in Mayo's account and the high level of contention and confusion regarding the interpretation of frequentist tests in the literature statistical and philosophical , the clarity this chapter provides is significant.
The account provided by Cox and Mayo is, of course, still frequentist. The primary argument Cox and Mayo provide for adopting the frequentist approach is the standard argument encountered in the literature: Responses to this argument are just as familiar. The error-statistical probabilities, while not subjective in interpretation, are influenced by how the investigator chooses to set up and analyse the experiment.
Surely, the issue at stake is not that the investigator has made assumptions, but whether these assumptions are explicit and justifiable. This is the argument for error-statistics based on the virtues of the error-statistical philosophy of science.