Null hypothesis significance testing has been one of the most frequently used statistical techniques in psychological research. However, the hypotheses considered in null hypothesis significant testing are usually either unrealistic or uninformative. In contrast, in the context of Bayesian statistics, researchers can directly evaluate the researchers' informative hypotheses by simple arithmetical calculations. In this paper, first the basic theory and practice of Bayesian evaluation of informative hypotheses are presented. Next, the specification of prior distributions is discussed. The paper concludes with a review of applications, which includes multiple regression, contingency tables, structural equation modeling, and meta-analysis.