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Frank Zenker
Nankai University
  1. A probabilistic analysis of argument cogency.David Godden & Frank Zenker - 2018 - Synthese 195 (4):1715-1740.
    This paper offers a probabilistic treatment of the conditions for argument cogency as endorsed in informal logic: acceptability, relevance, and sufficiency. Treating a natural language argument as a reason-claim-complex, our analysis identifies content features of defeasible argument on which the RSA conditions depend, namely: change in the commitment to the reason, the reason’s sensitivity and selectivity to the claim, one’s prior commitment to the claim, and the contextually determined thresholds of acceptability for reasons and for claims. Results contrast with, and (...)
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  2. Causal Argument.Ulrike Hahn, Frank Zenker & Roland Bluhm - 2017 - In Michael R. Waldmann (ed.), The Oxford Handbook of Causal Reasoning. New York, NY: Oxford University Press. pp. 475-494.
    In this chapter, we outline the range of argument forms involving causation that can be found in everyday discourse. We also survey empirical work concerned with the generation and evaluation of such arguments. This survey makes clear that there is presently no unified body of research concerned with causal argument. We highlight the benefits of a unified treatment both for those interested in causal cognition and those interested in argumentation, and identify the key challenges that must be met for a (...)
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    Cohen’s convention and the body of knowledge in behavioral science.Aran Arslan & Frank Zenker - manuscript
    In the context of discovery-oriented hypothesis testing research, behavioral scientists widely accept a convention for false positive (α) and false negative error rates (β) proposed by Jacob Cohen, who deemed the general relative seriousness of the antecedently accepted α = 0.05 to be matched by β = 0.20. Cohen’s convention not only ignores contexts of hypothesis testing where the more serious error is the β-error. Cohen’s convention also implies for discovery-oriented hypothesis testing research that a statistically significant observed effect is (...)
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