Is it time to shift the statistical lens from a Frequentist to a Bayesian paradigm in medical research?

In this article, we discuss two major statistical paradigms: Frequentist and Bayesian. Our objective is to explain the core concepts of each approach, which are often perceived as mathematically complex. Although these paradigms differ in their approaches to problem-solving, each offers distinct strengths. Frequentist methods rely solely on observed data and the concept of repeated experiments, whereas Bayesian methods combine observed data with prior knowledge to inform inference. We argue that modern medical research should incorporate both approaches into its analytical toolbox to ensure that a robust method of data analysis is selected on a case-by-case basis.