In statistical modelling, there is often a genuine interest to learn the most reasonable, parsimonious, and interpretable model that fits the data. This is especially true when faced with the oddly perplexing phenomenon of having “too much information” (data saturation). Model selection is indeed a vastly covered topic. In this talk, I will focus on the Bayesian approach to model selection, emphasising the selection of variables in a linear regression model. The outcome of the talk is three-fold: 1) To introduce the statistical framework for Bayesian variable selection; 2) to understand how we can use model probabilities as a basis for model selection; and 3) to demonstrate its application using real-world data (mortality and air pollution data). The hope is that the audience will gain an understanding of the method to possibly spur on further research and applications in their respective work.