The article discusses how Bayesian data analysis can be used to create hierarchical models, which are helpful for analyzing data with multiple levels. These models can describe data from individuals in groups within larger organizations. Bayesian methods are effective for estimating parameters in these complex models. The researchers provide examples using baseball batting averages and attention allocation in people with eating disorders. They also discuss how Bayesian model comparison can be applied to hierarchical modeling.