If you’ve spent much time statistically analyzing data, you know that the same statistical measurements can reflect very different underlying distributions. F.J. Anscombe created four data sets, now known as Anscombe’s quartet, to demonstrate how the same means, variances, correlations, and even linear regression lines can come from data sets that are obviously different — if you bother to graph them and take a look:
