The article discusses how Bayesian statistical modeling can help in selecting the best statistical models for data analysis. It explains the use of Bayesian analysis, probability, and Bayes' theorem in making these selections. The researchers explore different methods like Monte Carlo integration and Markov chain Monte Carlo for Bayesian inference. They also introduce various criteria for model selection, such as Bayesian information criterion and Bayesian model averaging. The study compares these criteria to determine the most effective approach for selecting the right statistical model.