Although a model can predict the expected outcome of an experiment, the residuals have a purely random nature, and consequently, the observable outcome is entirely unpredictable. However, it is usually assumed that, although random, the residuals follow a particular distribution of probability, allowing us to understand which outcomes are more likely to occur. This chapter introduces the Gaussian density function and demonstrates how it can be used to calculate probabilities and simulate future experimental outcomes, through the adoption of Monte Carlo methods.