Regression to the mean is an often misunderstood phenomena that routinely arises in both empirical research and in every day life. First described by Sir Francis Galton, regression to the mean is a process by which a measured observation that obtains an extreme value on one assessment will tend to obtain a less extreme value on a subsequent assessment, and vice versa. Galton found that, on average, taller fathers tended to have shorter sons, and shorter fathers tended to have taller sons. This same phenomenon has been observed in financial markets, standardized testing, child development, treatment outcome studies, and even professional sports. A recent article in the New York Times described this very process when exploring whether your favorite football team will get better or worse next season. Although an entertaining example, regression to the mean is critically important to fully understand in any longitudinal research study; Donald Campbell and David Kenny explore these issues in detail in their 1999 book A Primer on Regression Artifacts. Whether studying treatment interventions, high risk groups, high stakes testing, health outcomes, or any other topic of significance, regression to the mean must be understood, embraced, and mitigated. As Sir Francis Galton concluded in 1889, “Some people hate the very name of statistics but I find them full of beauty and interest. Whenever they are not brutalised, but handled by higher methods, and warily interpreted, their power of dealing with complicated phenomena is extraordinary.” His words still ring true more than a century later.