This work has at its basis three distinct areas of research: conditional statistics, correlational mathematics and robust mean-variance optimization.  The history and development around each in modern quantitative finance is briefly outlined, before introducing the Gerber Statistic.  The Gerber Statistic is a recently proposed co-movement measure. The measure is a conditional statistic and due to conditioning bias, will naturally differ from full-sample measures. We make use of an intuitive two-asset simulation framework to better elucidate the statistical behaviour of the Gerber Statistic, across its three forms. Using graphical correlation profiles, we explore the measure's behaviour across return conditioning threshold, sample size and market distribution, detailing its mechanisms of performance but also demonstrating several caveats around its understanding and use. We conclude that while interesting, the Gerber Statistic is best viewed as an imperfect conditional dependence metric.​