In this paper, the features of Bayesian hypothesis evaluation and statistical modelling are compared with those of the frequentist paradigm. Bayesian statistics enjoys the recent development of computers and software for data analysis. However, such benefit may cause two main problems. One problem is the neglect of effort to specify the details of statistical models or prior information, each of which is carefully considered in traditional statistical data analysis. The other problem is the futile exploration of models owing to flexible manipulation of probabilistic programming languages. Furthermore, for a fair comparison, this paper provides some situations that are suitable for Bayesian and frequentist statistics, respectively. The message of this paper is the importance of quantifying hypotheses and constructing statistical models as clearly as possible in a subjectively interpretable manner.
