Cerebral Palsy (CP) is one of the most common motor neurodevelopmental disorders which affects three of every thousand live births in North America. In this study, we use a Bayesian approach to advance the science of manual dexterity outcome measurement for young children. Fifty children with a diagnosis of CP and fifty healthy, typically developing children between the ages of 4 to 12 years of age were enrolled in the study. Firstly, the frequentist Analysis of Variance (ANOVA) method was used and then for comparison, a Bayesian Analysis of Variance (BANOVA) method was applied to analyze the various manual dexterity metrics obtained from a novel computer game-based assessment tool. Finally, the Bayesian Seemingly Unrelated Regression (BayesSUR) method was constructed to identify the influential factors affecting the manual dexterity performance matrix considering the response variables collectively, taking into account their interrelationships and dependencies. These findings indicate that Manual Ability Classification System (MACS) level emerges as a consistently influential factor for all response variables for both leftward and rightward movements, while gender predominantly influences the proportion of valid traces in rightward movements.