In this chapter, as the groundwork for learning causal discovery with graphical models, we introduce the basic concepts of probability and statistics. We begin by defining fundamental terms such as events, probabilities, and random variables and then introduce representative probability distributions: the binomial, normal, Poisson, and Gamma distributions. We also discuss concepts used to describe relations among multiple random variables—joint distributions, independence, correlation coefficients, and covariance matrices.