Introduction

To handle the uncertainty of real-world events, we can use probability to measure the likelihood of whether an event will occur. By definition, probability is quantified with a number between 0 and 1; the higher the probability (closer to 1), the more certain we are that an event will occur.

As statistical inference is used to deduce the properties of a given population, knowing the probability distribution of a given population becomes essential. For example, if you find that the data selected for prediction does not follow the exact assumed probability distribution in experiment design, the results should be refuted. In other words, probability provides justification for statistical inference.

In this chapter, we focus on the topic of probability distribution and simulation. We first discuss how to generate random samples, before covering how to use R to generate samples from various distributions such as normal, uniform, Poisson, chi-squared, and Student's t-distribution...