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There is a range of statistical approaches available to researchers. Nevertheless, in the probabilistic context, the frequentist approach is dominant, from the scientific literature to the teaching of statistical methods in higher education institutions. However, research questions are diverse, and other probabilistic statistical approaches may be advantageous in specific contexts. The methods used by researchers are derived mainly from their training. Unfortunately, alternative approaches, such as the Bayesian approach, are rarely taught, which may, in part, be due to the complexity of teaching them. This article aims to address this problem by presenting a series of fictitious examples illustrating the concepts behind Bayesian reasoning. It is intended as a tool for novice researchers looking to gain a basic understanding of the Bayesian approach. The prior, likelihood and posterior concepts will be illustrated by scenarios that learners can identify with. It is expected that novice researchers who have internalized the concepts of the Bayesian method, partly through these intuitive examples, would be more inclined to learn about this alternative statistical approach and consider using it in their research field. This could, in turn, help diversify the statistical methods used throughout the scientific literature.

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