In this tutorial, we introduce the notion of a Markov chain and explore how it can be used to sample from a large set of configurations. Our primary focus is determining how quickly a Markov chain "mixes," or converges to its stationary distribution, as this is the key factor in the running time. We provide an overview of several techniques used to establish good bounds on the mixing time. The applications are mostly chosen from statistical physics, although the methods are much more general.