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Deborah G. Mayo [46]Deborah G. Mayo [1]
  1. Deborah G. Mayo (2011). The Objective Epistemic Probabilist and the Severe Tester. In Gregory J. Morgan (ed.), Philosophy of Science Matters: The Philosophy of Peter Achinstein. Oxford University Press. 135.
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  2. David Cox & Deborah G. Mayo (2010). Objectivity and Conditionality in Frequentist Inference. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. 276.
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  3. Deborah G. Mayo (2010). An Error in the Argument From Conditionality and Sufficiency to the Likelihood Principle. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. 305.
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  4. Deborah G. Mayo (2010). Ad Hoc Save of a Theory of Adhocness? : Exchanges with John Worrall. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press.
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  5. Deborah G. Mayo (2010). Can Scientific Theories Be Warranted with Severity? : Exchanges with Alan Chalmers. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press.
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  6. Deborah G. Mayo (2010). Explanation and Testing Exchanges with Clark Glymour. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. 351.
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  7. Deborah G. Mayo (2010). Error and the Law : Exchanges with Larry Laudan. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. 397.
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  8. Deborah G. Mayo (2010). Learning From Error, Severe Testing, and the Growth of Theoretical Knowledge. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. 28.
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  9. Deborah G. Mayo (2010). Sins of the Epistemic Probabilist : Exchanges with Peter Achinstein. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. 189.
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  10. Deborah G. Mayo (2010). Toward Progressive Critical Rationalism : Exchanges with Alan Musgrave. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. 113.
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  11. Deborah G. Mayo & David Cox (2010). Frequentist Statistics as a Theory of Inductive Inference. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press.
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  12. Deborah G. Mayo & David Cox (2010). NewPerspectiveson (SomeOld) Problems of Frequentist Statistics. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. 247.
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  13. Deborah G. Mayo & Aris Spanos (eds.) (2010). Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press.
    Explores the nature of error and inference, drawing on exchanges on experimental reasoning, reliability, and the objectivity of science.
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  14. Deborah G. Mayo & Aris Spanos (2010). Introduction and Background. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press.
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  15. Deborah G. Mayo (2008). How to Discount Double-Counting When It Counts: Some Clarifications. British Journal for the Philosophy of Science 59 (4):857-879.
    The issues of double-counting, use-constructing, and selection effects have long been the subject of debate in the philosophical as well as statistical literature. I have argued that it is the severity, stringency, or probativeness of the test—or lack of it—that should determine if a double-use of data is admissible. Hitchcock and Sober ([2004]) question whether this ‘severity criterion' can perform its intended job. I argue that their criticisms stem from a flawed interpretation of the severity criterion. Taking their criticism as (...)
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  16. Deborah G. Mayo & Aris Spanos (2006). Philosophical Scrutiny of Evidence of Risks: From Bioethics to Bioevidence. Philosophy of Science 73 (5):803-816.
    We argue that a responsible analysis of today's evidence-based risk assessments and risk debates in biology demands a critical or metascientific scrutiny of the uncertainties, assumptions, and threats of error along the manifold steps in risk analysis. Without an accompanying methodological critique, neither sensitivity to social and ethical values, nor conceptual clarification alone, suffices. In this view, restricting the invitation for philosophical involvement to those wearing a "bioethicist" label precludes the vitally important role philosophers of science may be able to (...)
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  17. Deborah G. Mayo & Aris Spanos (2006). Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction. British Journal for the Philosophy of Science 57 (2):323-357.
    Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive inference as opposed to inductive behavior. We argue that the relevance of error probabilities (...)
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  18. Susan Haack, Rosa Mayorga, Jaime Nubiola, Cornelis de Waal, Deborah G. Mayo, Robert G. Meyers, Joseph C. Pitt & Nicholas Rescher (2005). 'Peirce-Pectives' on Metaphysics and the Sciences. Transactions of the Charles S. Peirce Society 41 (2):237-365.
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  19. Deborah G. Mayo (2005). Evidence as Passing Severe Tests: Highly Probable Versus Highly Probed Hypotheses. In P. Achinstein (ed.), Scientific Evidence: Philosophical Theories & Applications. The Johns Hopkins University Press. 95--128.
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  20. Deborah G. Mayo (2005). Peircean Induction and the Error-Correcting Thesis. Transactions of the Charles S. Peirce Society 41 (2):299 - 319.
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  21. Deborah G. Mayo & Aris Spanos (2004). Methodology in Practice: Statistical Misspecification Testing. Philosophy of Science 71 (5):1007-1025.
    The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actually met in the studies available, e.g., in psychology, ecology, biology, risk assessment. Philosophical scrutiny can (...)
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  22. Deborah G. Mayo (2003). Novel Work on Problems of Novelty? Comments on Hudson. Studies in History and Philosophy of Science Part B 34 (1):131-134.
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  23. Deborah G. Mayo (2001). Error and the Growth of Experimental Knowledge. International Studies in the Philosophy of Science 15 (1).
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  24. Deborah G. Mayo & Michael Kruse (2001). Principles of Inference and Their Consequences. In David Corfield & Jon Williamson (eds.), Foundations of Bayesianism. Kluwer Academic Publishers. 381--403.
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  25. Deborah G. Mayo (2000). Experimental Practice and an Error Statistical Account of Evidence. Philosophy of Science 67 (3):207.
    In seeking general accounts of evidence, confirmation, or inference, philosophers have looked to logical relationships between evidence and hypotheses. Such logics of evidential relationship, whether hypothetico-deductive, Bayesian, or instantiationist fail to capture or be relevant to scientific practice. They require information that scientists do not generally have (e.g., an exhaustive set of hypotheses), while lacking slots within which to include considerations to which scientists regularly appeal (e.g., error probabilities). Building on my co-symposiasts contributions, I suggest some directions in which a (...)
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  26. John Worrall, Deborah G. Mayo, J. J. C. Smart & Barry Barnes (2000). What is This Thing Called Philosophy of Science? Metascience 9 (2):172-198.
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  27. Deborah G. Mayo (1998). Some Problems with Chow's Problems with Power. Behavioral and Brain Sciences 21 (2):212-213.
    Chow correctly pinpoints several confusions in the criticisms of statistical hypothesis testing but his book is considerably weakened by its own confusions about concepts of testing (perhaps owing to an often very confusing literature). My focus is on his critique of power analysis (Ch. 6). Having denied that NHSTP considers alternative statistical hypotheses, and having been misled by a quotation from Cohen, Chow finds power analysis conceptually suspect.
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  28. Deborah G. Mayo (1997). Duhem's Problem, the Bayesian Way, and Error Statistics, or "What's Belief Got to Do with It?". Philosophy of Science 64 (2):222-244.
    I argue that the Bayesian Way of reconstructing Duhem's problem fails to advance a solution to the problem of which of a group of hypotheses ought to be rejected or "blamed" when experiment disagrees with prediction. But scientists do regularly tackle and often enough solve Duhemian problems. When they do, they employ a logic and methodology which may be called error statistics. I discuss the key properties of this approach which enable it to split off the task of testing auxiliary (...)
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  29. Deborah G. Mayo (1997). Error Statistics and Learning From Error: Making a Virtue of Necessity. 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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  30. Deborah G. Mayo (1997). Response to Howson and Laudan. 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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  31. Deborah G. Mayo (1997). Severe Tests, Arguing From Error, and Methodological Underdetermination. Philosophical Studies 86 (3):243-266.
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  32. Deborah G. Mayo (1996). Ducks, Rabbits, and Normal Science: Recasting the Kuhn's-Eye View of Popper's Demarcation of Science. British Journal for the Philosophy of Science 47 (2):271-290.
    Kuhn maintains that what marks the transition to a science is the ability to carry out ‘normal’ science—a practice he characterizes as abandoning the kind of testing that Popper lauds as the hallmark of science. Examining Kuhn's own contrast with Popper, I propose to recast Kuhnian normal science. Thus recast, it is seen to consist of severe and reliable tests of low-level experimental hypotheses (normal tests) and is, indeed, the place to look to demarcate science. While thereby vindicating Kuhn on (...)
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  33. Deborah G. Mayo (1994). The New Experimentalism, Topical Hypotheses, and Learning From Error. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994:270 - 279.
    An important theme to have emerged from the new experimentalist movement is that much of actual scientific practice deals not with appraising full-blown theories but with the manifold local tasks required to arrive at data, distinguish fact from artifact, and estimate backgrounds. Still, no program for working out a philosophy of experiment based on this recognition has been demarcated. I suggest why the new experimentalism has come up short, and propose a remedy appealing to the practice of standard error (...)
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  34. Deborah G. Mayo (1992). Did Pearson Reject the Neyman-Pearson Philosophy of Statistics? Synthese 90 (2):233 - 262.
    I document some of the main evidence showing that E. S. Pearson rejected the key features of the behavioral-decision philosophy that became associated with the Neyman-Pearson Theory of statistics (NPT). I argue that NPT principles arose not out of behavioral aims, where the concern is solely with behaving correctly sufficiently often in some long run, but out of the epistemological aim of learning about causes of experimental results (e.g., distinguishing genuine from spurious effects). The view Pearson did hold gives a (...)
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  35. Deborah G. Mayo (1991). Novel Evidence and Severe Tests. Philosophy of Science 58 (4):523-552.
    While many philosophers of science have accorded special evidential significance to tests whose results are "novel facts", there continues to be disagreement over both the definition of novelty and why it should matter. The view of novelty favored by Giere, Lakatos, Worrall and many others is that of use-novelty: An accordance between evidence e and hypothesis h provides a genuine test of h only if e is not used in h's construction. I argue that what lies behind the intuition that (...)
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  36. Deborah G. Mayo (1991). Philosophy of Science Association. In Richard Boyd, Philip Gasper & J. D. Trout (eds.), The Philosophy of Science. Mit Press. 58--4.
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  37. Deborah G. Mayo (1988). Toward a More Objective Understanding of the Evidence of Carcinogenic Risk. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1988:489 - 503.
    I argue that although the judgments required to reach statistical risk assessments may reflect policy values, it does not follow that the task of evaluating whether a given risk assessment is warranted by the evidence need also be imbued with policy values. What has led many to conclude otherwise, I claim, stems from misuses of the statistical testing methods involved. I set out rules for interpreting what specific test results do and do not say about the extent of a given (...)
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  38. Deborah G. Mayo (1987). How Everyone Can Have a Rare Property: Response to Sober on Frequency-Dependent Causation. Philosophy of Science 54 (2):266-276.
    In a recent discussion note Sober (1985) elaborates on the argument given in Sober (1982) to show the inadequacy of Ronald Giere's (1979, 1980) causal model for cases of frequency-dependent causation, and denies that Giere's (1984) response avoids the problem he raises. I argue that frequency-dependent effects do not pose a problem for Giere's original causal model, and that all parties in this dispute have been guity of misinterpreting the counterfactual populations involved in applying Giere's model.
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  39. Deborah G. Mayo & Norman L. Gilinsky (1987). Models of Group Selection. Philosophy of Science 54 (4):515-538.
    The key problem in the controversy over group selection is that of defining a criterion of group selection that identifies a distinct causal process that is irreducible to the causal process of individual selection. We aim to clarify this problem and to formulate an adequate model of irreducible group selection. We distinguish two types of group selection models, labeling them type I and type II models. Type I models are invoked to explain differences among groups in their respective rates of (...)
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  40. Deborah G. Mayo (1986). Cartwright, Causality, and Coincidence. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:42 - 58.
    Cartwright argues for being a realist about theoretical entities but non-realist about theoretical laws. Her reason is that while the former involves causal explanation, the latter involves theoretical explanation; and inferences to causes, unlike inferences to theories, can avoid the redundancy objection--that one cannot rule out alternatives that explain the phenomena equally well. I sketch Cartwright's argument for inferring the most probable cause, focusing on Perrin's inference to molecular collisions as the cause of Brownian motion. I argue that either the (...)
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  41. Deborah G. Mayo (1986). Understanding Frequency-Dependent Causation. Philosophical Studies 49 (1):109 - 124.
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  42. Deborah G. Mayo (1985). Behavioristic, Evidentialist, and Learning Models of Statistical Testing. Philosophy of Science 52 (4):493-516.
    While orthodox (Neyman-Pearson) statistical tests enjoy widespread use in science, the philosophical controversy over their appropriateness for obtaining scientific knowledge remains unresolved. I shall suggest an explanation and a resolution of this controversy. The source of the controversy, I argue, is that orthodox tests are typically interpreted as rules for making optimal decisions as to how to behave--where optimality is measured by the frequency of errors the test would commit in a long series of trials. Most philosophers of statistics, however, (...)
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  43. Deborah G. Mayo (1983). An Objective Theory of Statistical Testing. Synthese 57 (3):297 - 340.
    Theories of statistical testing may be seen as attempts to provide systematic means for evaluating scientific conjectures on the basis of incomplete or inaccurate observational data. The Neyman-Pearson Theory of Testing (NPT) has purported to provide an objective means for testing statistical hypotheses corresponding to scientific claims. Despite their widespread use in science, methods of NPT have themselves been accused of failing to be objective; and the purported objectivity of scientific claims based upon NPT has been called into question. The (...)
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  44. Deborah G. Mayo (1982). About Thinking. Teaching Philosophy 5 (1):80-83.
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  45. Deborah G. Mayo (1982). On After-Trial Criticisms of Neyman-Pearson Theory of Statistics. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1982:145 - 158.
    Despite its widespread use in science, the Neyman-Pearson Theory of Statistics (NPT) has been rejected as inadequate by most philosophers of induction and statistics. They base their rejection largely upon what the author refers to as after-trial criticisms of NPT. Such criticisms attempt to show that NPT fails to provide an adequate analysis of specific inferences after the trial is made, and the data is known. In this paper, the key types of after-trial criticisms are considered and it is argued (...)
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  46. Deborah G. Mayo (1981). In Defense of the Neyman-Pearson Theory of Confidence Intervals. Philosophy of Science 48 (2):269-280.
    In Philosophical Problems of Statistical Inference, Seidenfeld argues that the Neyman-Pearson (NP) theory of confidence intervals is inadequate for a theory of inductive inference because, for a given situation, the 'best' NP confidence interval, [CIλ], sometimes yields intervals which are trivial (i.e., tautologous). I argue that (1) Seidenfeld's criticism of trivial intervals is based upon illegitimately interpreting confidence levels as measures of final precision; (2) for the situation which Seidenfeld considers, the 'best' NP confidence interval is not [CIλ] as Seidenfeld (...)
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  47. Deborah G. Mayo (1980). The Philosophical Relevance of Statistics. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1980:97 - 109.
    While philosophers have studied probability and induction, statistics has not received the kind of philosophical attention mathematics and physics have. Despite increasing use of statistics in science, statistical advances have been little noted in the philosophy of science literature. This paper shows the relevance of statistics to both theoretical and applied problems of philosophy. It begins by discussing the relevance of statistics to the problem of induction and then discusses the reasoning that leads to causal generalizations and how statistics elucidates (...)
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