Bayesian Approach: Adding Clinical Edge in Interpreting Medical Data

Abstract

In frequentist tests, the significance testing framework for null hypothesis permits dichotomous conclusions alone, and such tests do not quantify the strength of the evidence supporting the null hypothesis. Under the Bayesian approach, probability reflects their uncertainty or degree of belief, that is, how scientific belief should be modified by data. This paper attempts to demonstrate the advantages of the Bayes factor in hypothesis testing that can quantify evidence in favour of the null hypothesis and how the prior specification is used for statistical tools, such as independent t-test and Analysis of Variance (ANOVA). Despite the advantages of the Bayesian approach, the use of conventional tests that rely on inference by p-values is ubiquitous in medical research. The adoption of the Bayesian approach may be seriously hindered by the absence of formulae, algorithms, etc. Furthermore, we have attempted to validate our argument by interpreting the application of both the Frequentist and Bayesian approaches for dietary intake of calcium mg/day with the help of JASP software.