In this paper, we propose methods to estimate the credibility of reviewers as an individual and as a group, where the credibility is defined as the ability of precisely estimating the quality of items. Our proposed methods are built on two simple assumptions: 1) a reviewer who has reviewed many and diverse items has high credibility, and 2) a group of reviewers is credible if the group consists of many and diverse reviewers. To verify the two assumptions, we conducted experiments with a movie review dataset. The experimental results showed that the diversity of reviewed items and reviewers was effective to estimate the credibility of reviewers and reviewer groups, respectively. Therefore, yes, the diversity does improve the credibility of user review data.