The article discusses how choosing the right prior distribution is crucial in Bayesian analysis. Common methods for choosing priors are often influenced by the likelihood of the data, creating a conceptual challenge. The researchers propose a new approach that considers the entire Bayesian analysis process, from inference to prediction to model evaluation, to resolve this issue.