This article revisits how the end points of plotted line segments should be selected when graphing interactions involving a continuous target predictor variable. Under the standard approach, end points are chosen at ±1 or 2 standard deviations from the target predictor mean. However, when the target predictor and moderator are correlated or the conditional variance of the target predictor depends on the moderator variable value, these end points may reside in regions with little or no supporting data, encouraging potentially erroneous interpretations of the interaction, in particular, and patterns in the data, in general. Tumble graphs are introduced to minimize the likelihood of these problems. The utility of the Tumble graph over the standard approach is demonstrated with a real data example.