Oftentimes, instead of having a single list or 1-dimensional data set to analyze, an analyst will have multiple related data sets to track and compare. For example, imagine you had a 1-dimensional data set of the daily high temperatures in Chicago along with the number of incidents of heatstroke reported at area hospitals. Or, perhaps you have the prices of two related stocks, both of which vary as a function of time. It is useful to compare these data sets and to examine whether variations in one are connected to variations in the other. Comparisons can illuminate links between the data sets, causal or otherwise, and can confirm or contradict your understanding of the system of interest. The techniques I will discuss in this chapter are flexible enough to apply to a variety of situations and the analyst’s imagination is the only limit when it comes to designing comparisons.