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  1. Philosophy of science and the replicability crisis.Felipe Romero - 2019 - Philosophy Compass 14 (11):e12633.
    Replicability is widely taken to ground the epistemic authority of science. However, in recent years, important published findings in the social, behavioral, and biomedical sciences have failed to replicate, suggesting that these fields are facing a “replicability crisis.” For philosophers, the crisis should not be taken as bad news but as an opportunity to do work on several fronts, including conceptual analysis, history and philosophy of science, research ethics, and social epistemology. This article introduces philosophers to these discussions. First, I (...)
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  • Novelty versus Replicability: Virtues and Vices in the Reward System of Science.Felipe Romero - 2017 - Philosophy of Science 84 (5):1031-1043.
    The reward system of science is the priority rule. The first scientist making a new discovery is rewarded with prestige, while second runners get little or nothing. Michael Strevens, following Philip Kitcher, defends this reward system, arguing that it incentivizes an efficient division of cognitive labor. I argue that this assessment depends on strong implicit assumptions about the replicability of findings. I question these assumptions on the basis of metascientific evidence and argue that the priority rule systematically discourages replication. My (...)
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  • From pre-registration to publication: a non-technical primer for conducting a meta-analysis to synthesize correlational data.Daniel S. Quintana - 2015 - Frontiers in Psychology 6.
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  • Theory of Probability.Harold Jeffreys - 1940 - Philosophy of Science 7 (2):263-264.
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  • Can the Behavioral Sciences Self-correct? A Social Epistemic Study.Felipe Romero - 2016 - Studies in History and Philosophy of Science Part A 60 (C):55-69.
    Advocates of the self-corrective thesis argue that scientific method will refute false theories and find closer approximations to the truth in the long run. I discuss a contemporary interpretation of this thesis in terms of frequentist statistics in the context of the behavioral sciences. First, I identify experimental replications and systematic aggregation of evidence (meta-analysis) as the self-corrective mechanism. Then, I present a computer simulation study of scientific communities that implement this mechanism to argue that frequentist statistics may converge upon (...)
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  • Bias and values in scientific research.Torsten Wilholt - 2009 - Studies in History and Philosophy of Science Part A 40 (1):92-101.
    When interests and preferences of researchers or their sponsors cause bias in experimental design, data interpretation or dissemination of research results, we normally think of it as an epistemic shortcoming. But as a result of the debate on science and values, the idea that all extra-scientific influences on research could be singled out and separated from pure science is now widely believed to be an illusion. I argue that nonetheless, there are cases in which research is rightfully regarded as epistemologically (...)
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  • Testing a precise null hypothesis: the case of Lindley’s paradox.Jan Sprenger - 2013 - Philosophy of Science 80 (5):733-744.
    The interpretation of tests of a point null hypothesis against an unspecified alternative is a classical and yet unresolved issue in statistical methodology. This paper approaches the problem from the perspective of Lindley's Paradox: the divergence of Bayesian and frequentist inference in hypothesis tests with large sample size. I contend that the standard approaches in both frameworks fail to resolve the paradox. As an alternative, I suggest the Bayesian Reference Criterion: it targets the predictive performance of the null hypothesis in (...)
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  • Bias in Peer Review.Carole J. Lee, Cassidy R. Sugimoto, Guo Zhang & Blaise Cronin - 2013 - Journal of the American Society for Information Science and Technology 64 (1):2-17.
    Research on bias in peer review examines scholarly communication and funding processes to assess the epistemic and social legitimacy of the mechanisms by which knowledge communities vet and self-regulate their work. Despite vocal concerns, a closer look at the empirical and methodological limitations of research on bias raises questions about the existence and extent of many hypothesized forms of bias. In addition, the notion of bias is predicated on an implicit ideal that, once articulated, raises questions about the normative implications (...)
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  • The logic of tests of significance.Stephen Spielman - 1974 - Philosophy of Science 41 (3):211-226.
    In spite of the fact that the Neyman-Pearson theory of testing is the official theory of statistical testing, most research publications in the social sciences use a pattern of inductive reasoning that is characteristic of Fisherian tests of significance. The exact structure and rationale of this pattern of reasoning is widely misunderstood. The goal of the paper is to describe precisely the pattern and its rationale, and to show that while it is far more cogent than Fisher's critics have realized, (...)
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  • Who Should Do Replication Labor?Felipe Romero - 2018 - Advances in Methods and Practices in Psychological Science 1 (4):516-537.
    . Scientists, for the most part, want to get it right. However, the social structures that govern their work undermine that aim, and this leads to nonreplicable findings in many fields. Because the social structure of science is a decentralized system, it is difficult to intervene. In this article, I discuss how we might do so, focusing on self-corrective-labor schemes. First, I argue that we need to implement a scheme that makes replication work outcome independent, systematic, and sustainable. Second, I (...)
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  • Why Most Published Research Findings Are False.John P. A. Ioannidis - 2005 - PLoS Med 2 (8):e124.
    Published research findings are sometimes refuted by subsequent evidence, says Ioannidis, with ensuing confusion and disappointment.
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  • (2 other versions)Error and the growth of experimental knowledge.Deborah Mayo - 1996 - International Studies in the Philosophy of Science 15 (1):455-459.
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  • Review. [REVIEW]Barry Gower - 1997 - British Journal for the Philosophy of Science 48 (1):555-559.
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  • The importance of proving the null.C. R. Gallistel - 2009 - Psychological Review 116 (2):439-453.
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