The article explores how prior information affects the results of Bayesian analyses. By using copulas, researchers can create more flexible prior distributions. However, as data is observed, the posterior distribution may not retain these flexible structures. Despite this, copula-based priors are still useful for designing studies. Choosing the right copula can impact how well the posterior distribution converges. Recommendations are made to simplify the process of specifying prior dependence for future analyses.