Basic concepts surrounding probability theory and statistics are discussed, beginning with an introduction of experiments, sample spaces, and events. Then, the idea of random variables and probability distributions are introduced, along with the differences between the continuous and discrete cases and thus also probability density functions and probability mass functions. Concepts surrounding conditional probability, dependence, joint distributions, expectation, and variance are also discussed. The important theorems of probability, namely the law of large numbers and the central limit theorem, are also introduced, along with differences between the frequentist and Bayesian interpretations of probability, before moving on to concepts from statistics. Statistical topics introduced include point estimates, confidence intervals, hypothesis testing, and p-values, including frequentist and Bayesian perspectives on these topics. The chapter ends with a brief discussion of topics in modern statistics.