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  1. Objectivity and Underdetermination in Statistical Model Selection.Beckett Sterner & Scott Lidgard - 2024 - British Journal for the Philosophy of Science 75 (3):717-739.
    The growing range of methods for statistical model selection is inspiring new debates about how to handle the potential for conflicting results when different methods are applied to the same data. While many factors enter into choosing a model selection method, we focus on the implications of disagreements among scientists about whether, and in what sense, the true probability distribution is included in the candidate set of models. While this question can be addressed empirically, the data often provide inconclusive results (...)
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  • The New Logic of Willard Van Orman Quine and its Significance for the Success of Logic in Brazil.Julio Michael Stern - 2019 - Logic and Logical Philosophy 28:789-793.
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  • Duhem’s problem revisited: logical versus epistemic formulations and solutions.Michael Dietrich & Phillip Honenberger - 2020 - Synthese 197 (1):337-354.
    When the results of an experiment appears to disconfirm a hypothesis, how does one know whether it’s the hypothesis, or rather some auxiliary hypothesis or assumption, that is at fault? Philosophers’ answers to this question, now known as “Duhem’s problem,” have differed widely. Despite these differences, we affirm Duhem’s original position that the logical structure of this problem alone does not allow a solution. A survey of philosophical approaches to Duhem’s problem indicates that what allows any philosopher, or scientists for (...)
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  • Responses to Fitelson, Sansom, and Sarkar. [REVIEW]Elliott Sober - 2011 - Philosophy and Phenomenological Research 83 (3):692-704.
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  • Evidence-Based Darwinism: Evidence and Evolution: The Logic Behind the Science Elliott Sober Cambridge: Cambridge University Press, 2008. [REVIEW]Gregory Radick - 2010 - Biological Theory 5 (3):289-291.
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  • Theory-laden observation and incommensurability.Mehmet Elgin - 2008 - Organon F: Medzinárodný Časopis Pre Analytickú Filozofiu 15 (1):3-19.
    In this paper, I investigate the logical relation between two claims: observations are theory-laden1 and there is no empirical common ground upon which to evaluate successive scientific theories that belong to different paradigms. I, first, construct an argument where is the main premise and is the conclusion. I argue that the term „theory-laden” has three distinct senses: semantic, psychological and epistemic. If ‘theory-laden’ is understood in either epistemic or psychological senses, then the conclusion becomes a claim about people. If incommensurability (...)
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  • Bayesian confirmation and auxiliary hypotheses revisited: A reply to Strevens.Branden Fitelson & Andrew Waterman - 2005 - British Journal for the Philosophy of Science 56 (2):293-302.
    has proposed an interesting and novel Bayesian analysis of the Quine-Duhem (Q–D) problem (i.e., the problem of auxiliary hypotheses). Strevens's analysis involves the use of a simplifying idealization concerning the original Q–D problem. We will show that this idealization is far stronger than it might appear. Indeed, we argue that Strevens's idealization oversimplifies the Q–D problem, and we propose a diagnosis of the source(s) of the oversimplification. Some background on Quine–Duhem Strevens's simplifying idealization Indications that (I) oversimplifies Q–D Strevens's argument (...)
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  • Accommodation, prediction and replication: model selection in scale construction.Clayton Peterson - 2019 - Synthese 196 (10):4329-4350.
    In psychology, measurement instruments are constructed from scales, which are obtained on the grounds of exploratory and confirmatory factor analysis. Looking at the literature, one can find various recommendations regarding how these techniques should be used during the scale construction process. Some authors suggest to use exploratory factor analysis on the entire data set while others advice to perform an internal cross-validation by randomly splitting the data set in two and then either perform exploratory factor analysis on both parts or (...)
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  • Probability in fine-tuning design arguments.Kent Staley - unknown
    This paper examines probabilistic versions of the fine-tuning argument for design (FTA), with an emphasis on the interpretation of the probability statements involved in such arguments. Three categories of probability are considered: physical, epistemic, and logical. Of the three possibilities, I argue that only logical probability could possibly support a cogent probabilistic FTA. However, within that framework, the premises of the argument require a level of justification that has not been met, and, it is reasonable to believe, will not be (...)
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