Rough set based rule induction methods have been applied to knowledge discovery in databases. The empirical results obtained show that they are very powerful and that some important knowledge has been extracted from datasets. However, quantitative evaluation of induced rules are based not on statistical evidence but on rather naive indices, such as conditional probabilities and functions of conditional probabilities. In this paper, we introduce a new approach to induced rules for qnantitative evaluation, which can he viewed as a statistical extention of rough set methods. For this extension, chi-square distribution and F-distribution play a n important role in statistical evaluation.