The main objective of this case study is to acquaint the reader with weighting: a statistical technique used to compensate for inaccuracies in sample data. The vehicle is a study we conducted that examined the potential of comic superheroes as a tool for the empowerment of children. Our study examined the prevalence of childhood adversities (e.g., bullying, loss of parents, economic limitations) among a sample of the top-20 comic superheroes, rank-ordered by worldwide gross film earnings. As there was variability in earnings among the superheroes in our sample, we employed a weighting algorithm to estimate prevalences of individual childhood adversities among the sample as a whole. This algorithm gave more weight to the higher-earning comic superheroes (e.g., Spider-Man, Batman) and less to their lower-earning counterparts (e.g., Catwoman, Green Lantern). In addition to detailing the implementation of weighting for our study, we offer descriptions of its use in other types of studies, including its usage in meta-analysis. Our goal is that the reader gains a fundamental understanding of weighting and its role in statistical analysis.