As a convention, a p-value is often computed in hypothesis testing and compared with the nominal level of 0.05 to determine whether to reject the null hypothesis. Although the smaller the p-value, the more significant the statistical test, it is difficult to perceive the p-value in a probability scale and quantify it as the strength of the data against the null hypothesis. In contrast, the Bayesian posterior probability of the null hypothesis has an explicit interpretation of how strong the data support the null. We make a comparison of the p-value and the posterior probability by considering a recent clinical trial. The results show that even when we reject the null hypothesis, there is still a substantial probability (around 20%) that the null is true. Not only should we examine whether the data would have rarely occurred under the null hypothesis, but we also need to know whether the data would be rare under the alternative. As a result, the p-value only provides one side of the information, for which the Bayes factor and posterior probability may offer complementary evidence.