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  1. Statistical dogma and the logic of significance testing.Stephen Spielman - 1978 - Philosophy of Science 45 (1):120-135.
    In a recent note Roger Carlson presented a rather negative appraisal of my treatment of the logic of Fisherian significance testing in [10]. The main issue between us involves Carlson's thesis that, within the limits set by Fisher, standard significance tests are valuable tools of data analysis as they stand, i.e., without modification of the structure of the reasoning they employ. Call this the adequacy thesis. In my paper I argued that the pattern of reasoning employed by tests of significance (...)
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  • Support.R. D. Rosenkrantz - 1977 - Synthese 36 (2):181 - 193.
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  • Scientific self-correction: the Bayesian way.Felipe Romero & Jan Sprenger - 2020 - Synthese (Suppl 23):1-21.
    The enduring replication crisis in many scientific disciplines casts doubt on the ability of science to estimate effect sizes accurately, and in a wider sense, to self-correct its findings and to produce reliable knowledge. We investigate the merits of a particular countermeasure—replacing null hypothesis significance testing with Bayesian inference—in the context of the meta-analytic aggregation of effect sizes. In particular, we elaborate on the advantages of this Bayesian reform proposal under conditions of publication bias and other methodological imperfections that are (...)
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  • Scientific self-correction: the Bayesian way.Felipe Romero & Jan Sprenger - 2020 - Synthese 198 (S23):5803-5823.
    The enduring replication crisis in many scientific disciplines casts doubt on the ability of science to estimate effect sizes accurately, and in a wider sense, to self-correct its findings and to produce reliable knowledge. We investigate the merits of a particular countermeasure—replacing null hypothesis significance testing with Bayesian inference—in the context of the meta-analytic aggregation of effect sizes. In particular, we elaborate on the advantages of this Bayesian reform proposal under conditions of publication bias and other methodological imperfections that are (...)
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  • Die Falsifikation Statistischer Hypothesen/The falsification of statistical hypotheses.Max Albert - 1992 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 23 (1):1-32.
    It is widely held that falsification of statistical hypotheses is impossible. This view is supported by an analysis of the most important theories of statistical testing: these theories are not compatible with falsificationism. On the other hand, falsificationism yields a basically viable solution to the problems of explanation, prediction and theory testing in a deterministic context. The present paper shows how to introduce the falsificationist solution into the realm of statistics. This is done mainly by applying the concept of empirical (...)
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  • The logic of tests of significance.Roger Carlson - 1976 - Philosophy of Science 43 (1):116-128.
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  • Resolving Neyman's paradox.Max Albert - 2002 - British Journal for the Philosophy of Science 53 (1):69-76.
    According to Fisher, a hypothesis specifying a density function for X is falsified (at the level of significance ) if the realization of X is in the size- region of lowest densities. However, non-linear transformations of X can map low-density into high-density regions. Apparently, then, falsifications can always be turned into corroborations (and vice versa) by looking at suitable transformations of X (Neyman's Paradox). The present paper shows that, contrary to the view taken in the literature, this provides no argument (...)
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  • Statistical Significance Testing in Economics.William Peden & Jan Sprenger - 2021 - In Conrad Heilmann & Julian Reiss (eds.), The Routledge Handbook of the Philosophy of Economics.
    The origins of testing scientific models with statistical techniques go back to 18th century mathematics. However, the modern theory of statistical testing was primarily developed through the work of Sir R.A. Fisher, Jerzy Neyman, and Egon Pearson in the inter-war period. Some of Fisher's papers on testing were published in economics journals (Fisher, 1923, 1935) and exerted a notable influence on the discipline. The development of econometrics and the rise of quantitative economic models in the mid-20th century made statistical significance (...)
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  • 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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