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  1. Framing the Epistemic Schism of Statistical Mechanics.Javier Anta - 2021 - Proceedings of the X Conference of the Spanish Society of Logic, Methodology and Philosophy of Science.
    In this talk I present the main results from Anta (2021), namely, that the theoretical division between Boltzmannian and Gibbsian statistical mechanics should be understood as a separation in the epistemic capabilities of this physical discipline. In particular, while from the Boltzmannian framework one can generate powerful explanations of thermal processes by appealing to their microdynamics, from the Gibbsian framework one can predict observable values in a computationally effective way. Finally, I argue that this statistical mechanical schism contradicts the Hempelian (...)
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  • The quantitative problem of old evidence.E. C. Barnes - 1999 - British Journal for the Philosophy of Science 50 (2):249-264.
    The quantitative problem of old evidence is the problem of how to measure the degree to which e confirms h for agent A at time t when A regards e as justified at t. Existing attempts to solve this problem have applied the e-difference approach, which compares A's probability for h at t with what probability A would assign h if A did not regard e as justified at t. The quantitative problem has been widely regarded as unsolvable primarily on (...)
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  • Error statistics and learning from error: Making a virtue of necessity.Deborah G. Mayo - 1997 - Philosophy of Science 64 (4):212.
    The error statistical account of testing uses statistical considerations, not to provide a measure of probability of hypotheses, but to model patterns of irregularity that are useful for controlling, distinguishing, and learning from errors. The aim of this paper is (1) to explain the main points of contrast between the error statistical and the subjective Bayesian approach and (2) to elucidate the key errors that underlie the central objection raised by Colin Howson at our PSA 96 Symposium.
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  • What are the drivers of induction? Towards a Material Theory+.Julian Reiss - 2020 - Studies in History and Philosophy of Science Part A 83 (C):8-16.
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  • Sins of the epistemic probabilist : exchanges with Peter Achinstein.Deborah G. Mayo - 2009 - In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. New York: Cambridge University Press. pp. 189.
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  • Acceptibility, Evidence, and Severity.Prasanta S. Bandyopadhyay & Gordon G. Brittan - 2006 - Synthese 148 (2):259-293.
    The notion of a severe test has played an important methodological role in the history of science. But it has not until recently been analyzed in any detail. We develop a generally Bayesian analysis of the notion, compare it with Deborah Mayo’s error-statistical approach by way of sample diagnostic tests in the medical sciences, and consider various objections to both. At the core of our analysis is a distinction between evidence and confirmation or belief. These notions must be kept separate (...)
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  • Does Science Presuppose Naturalism ?Yonatan I. Fishman & Maarten Boudry - 2013 - Science & Education 22 (5):921-949.
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  • Kettlewell from an error statisticians's point of view.David Wÿss Rudge - 2001 - Perspectives on Science 9 (1):59-77.
    : Bayesians and error statisticians have relied heavily upon examples from physics in developing their accounts of scientific inference. The present essay demonstrates it is possible to analyze H.B.D. Kettlewell's classic study of natural selection from Deborah Mayo's error statistical point of view (Mayo 1996). A comparison with a previous analysis of this episode from a Bayesian perspective (Rudge 1998) reveals that the error statistical account makes better sense of investigations such as Kettlewell's because it clarifies how core elements in (...)
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  • Response to Howson and Laudan.Deborah G. Mayo - 1997 - Philosophy of Science 64 (2):323-333.
    A toast is due to one who slays Misguided followers of Bayes, And in their heart strikes fear and terror With probabilities of error! (E.L. Lehmann).
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