Besides checking that the statistical properties are still valid, we also check to see if the point has moved closer to the target shape. To move the points towards a particular shape, we perform an additional check at each random perturbation. In the animation below, we show the process of 200,000 iterations of perturbations towards a 'circle' shape: We accomplish this by biasing the random point movements towards a particular shape. However, as mentioned above, in order for these datasets to be effective tools for underscoring the importance of visualizing your data, they need to be visually distinct and clearly different. Repeating this subtle 'perturbation' process enough times, results in a completely different dataset. 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. 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. 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. 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. However, after visualizing (plotting) the data, it becomes clear that the datasets are markedly different. 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. An effective (and often used) tool used to demonstrate that visualizing your data is in fact important is Anscome's Quartet.

Some people are of the impression that charts are simply 'pretty pictures', while all of the important information can be divined through statistical analysis. It can be difficult to demonstrate the importance of data visualization.

Plot two datasets on same graph r