Fig 1. Anscombe's Quartet (left), and a "Unstructured Quartet" on the right, where the datasets have the same summary statistics as those in Anscombe's Quartet, but lack underlying structure or visual distinction.

It can be difficult to demonstrate the importance of data visualization. Some people are of the impression that charts are simply "pretty pictures", while all of the important information can be divined through statistical analysis. An effective (and often used) tool used to demonstrate that visualizing your data is in fact important is Anscome's Quartet. Developed by F.J. Anscombe in 1973, Anscombe's Quartet is a set of four datasets, where each produces the same summary statistics (mean, standard deviation, and correlation), which could lead one to believe the datasets are quite similar. However, after visualizing (plotting) the data, it becomes clear that the datasets are markedly different. The effectiveness of Anscombe's Quartet is not due to simply having four different datasets which generate the same statistical properties, it is that four clearly different and visually distinct datasets are producing the same statistical properties. In contrast the "Unstructured Quartet" on the right in Figure 1 also shares the same statistical properties as Anscombe's Quartet, however without any obvious underlying structure to the individual datasets, this quartet is not nearly as effective at demonstrating the importance of visualizing your data. 

While very popular and effective for illustrating the importance of visualizing your data, they have been around for nearly 45 years, and it is not known how Anscombe came up with his datasets. So, we developed a technique to create these types of datasets – those which are identical over a range of statistical properties, yet produce dissimilar graphics.