The capacity of Bayesian methods in estimating complex statistical models is undeniable. Bayesian data analysis is seen as having a range of advantages, such as an intuitive probabilistic interpretation of the parameters of interest, the efficient incorporation of prior information to empirical data analysis, model averaging and model selection. As a simplified demonstration, we illustrate (1) how Bayesians test and compare two non-nested growth curve models using Bayesian estimation with non-informative prior; (2) how Bayesians model and handle missing outcomes in the context of missing values; and (3) how Bayesians incorporate data-based evidence from a previous data set, construct informative priors and treat them as extra information while conducting an up-to-date analogy analysis. (Contains 7 tables, 5 figures, and 5 notes.)