David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Philosophy of Science 52 (2):256-273 (1985)
In clinical and agricultural trials, there is the danger that an experimental outcome appears to arise from the causal process or treatment one is interested in when, in reality, it was produced by some extraneous variation in the experimental conditions. The remedy prescribed by classical statisticians involves the procedure of randomization, whose effectiveness and appropriateness is criticized. An alternative, Bayesian analysis of experimental design, is shown, on the other hand, to provide a coherent and intuitively satisfactory solution to the problem
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Robyn Bluhm (2010). The Epistemology and Ethics of Chronic Disease Research: Further Lessons From Ecmo. Theoretical Medicine and Bioethics 31 (2):107-122.
Andrew Ward & Pamela Jo Johnson (2008). Addressing Confounding Errors When Using Non-Experimental, Observational Data to Make Causal Claims. Synthese 163 (3):419 - 432.
Kristin Shrader-Frechette (2011). Randomization and Rules for Causal Inferences in Biology: When the Biological Emperor (Significance Testing) Has No Clothes. Biological Theory 6 (2):154-161.
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