Data often arrives sequentially, rather than as a collection. When this occurs – or when the experiment can be designed so that this occurs – there can be a considerable advantage in using statistical methods, called sequential analysis, that are tailored to such situations. Classical frequentist statistical methods require analysis with a pre-specified collection of data, and hence cannot be used in such sequential settings. Interestingly, Bayesian methods can be directly used in sequential settings.