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We are All Bayesian, Everyone is Not a Bayesian

Andreoletti, Mattia and Oldofredi, Andrea (2018) We are All Bayesian, Everyone is Not a Bayesian. Topoi, 38 (2). pp. 477-485.

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Abstract

Medical research makes intensive use of statistics in order to support its claims. In this paper we make explicit an epistemological tension between the conduct of clinical trials and their interpretation: statistical evidence is sometimes discarded on the basis of an (often) underlined Bayesian reasoning. We suggest that acknowledging the potentiality of Bayesian statistics might contribute to clarify and improve comprehension of medical research. Nevertheless, despite Bayesianism may provide a better account for scientific inference with respect to the standard frequentist approach, Bayesian statistics is rarely adopted in clinical research. The main reason lies in the supposed subjective elements characterizing this perspective. Hence, we discuss this objection presenting the so-called Reference analysis, a formal method which has been developed in the context of objective Bayesian statistics in order to define priors which have a minimal or null impact on posterior probabilities. Furthermore, according to this method only available data are relevant sources of information, so that it resists the most common criticisms against Bayesianism.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Andreoletti, Mattia
Oldofredi, AndreaAndrea.Oldofredi@unil.ch
Keywords: Evidence; Clinical trials; Scientific inference; Frequentism; Bayesian statistics; Reference analysis
Subjects: General Issues > Evidence
Specific Sciences > Probability/Statistics
General Issues > Science and Policy
Depositing User: Dr. Andrea Oldofredi
Date Deposited: 28 Oct 2019 02:33
Last Modified: 28 Oct 2019 02:33
Item ID: 16582
Journal or Publication Title: Topoi
Publisher: Springer
Official URL: https://link.springer.com/article/10.1007%2Fs11245...
DOI or Unique Handle: https://doi.org/10.1007/s11245-018-9554-4
Subjects: General Issues > Evidence
Specific Sciences > Probability/Statistics
General Issues > Science and Policy
Date: 2018
Page Range: pp. 477-485
Volume: 38
Number: 2
URI: http://philsci-archive.pitt.edu/id/eprint/16582

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