Data analyses should reveal truths about data. To the extent possible analyses should tell a complete picture. Data analyses should not inadvertently ignore phenomena that might be discovered in sample data sets. However, common univariate or multivariate data analysis methods tend to be based on only the means, standard deviations, and Pearson correlations. The result is that many important truths are discovered, but not the whole truth. This article illustrates in a sample data set that (a) data analyses of other properties of variables and groups are feasible and practical, and (b) such analyses may reveal important information not otherwise detectable. These extensions of common statistical methods are applicable to data analysis and interpretation issues in the social and behavioral sciences.