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The Contribution of Bayesian Methods in Solving the Paradoxes of Classical Statistical Tests in Biomedical Research
by
Meyer, Nicolas
in
Biomedical research
/ Hypotheses
/ Intensive care
/ Methods
/ Statistics
/ Tutorial
2026
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The Contribution of Bayesian Methods in Solving the Paradoxes of Classical Statistical Tests in Biomedical Research
by
Meyer, Nicolas
in
Biomedical research
/ Hypotheses
/ Intensive care
/ Methods
/ Statistics
/ Tutorial
2026
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The Contribution of Bayesian Methods in Solving the Paradoxes of Classical Statistical Tests in Biomedical Research
Journal Article
The Contribution of Bayesian Methods in Solving the Paradoxes of Classical Statistical Tests in Biomedical Research
2026
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Overview
Almost all publications in biomedical literature have employed statistical tests, with p-values being considered of particular importance in the assessment of the presence of a link between two variables. However, these tests and p-values have been the subject of considerable criticism. It may appear paradoxical that tools utilised by the scientific community for nearly a century could possess all the flaws attributed to them. This paradox can partially be explained by the counterintuitive nature of p-values and the fact that the test that generates them is the result of a combination of two tests that were developed to answer statistical questions of a very different nature. The respective characteristics of these two tests are essentially unknown to the majority of users of p-values. The aforementioned paradox can be partially explained by the paucity of publications that seek to elucidate these concepts for users of p-values, the majority of whom are not statisticians. The recently introduced Bayesian methods have properties that enable us to understand the limitations of traditional methods. In Bayesian methods, the use of a specific interpretation of probability allows for better exploitation of clinical research data. The aim of this article is to highlight the limits of non-Bayesian methods and explain the principles and functioning of Bayesian methods to a non-statistical audience.
Publisher
MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
Subject
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