- Training and test sets are used to measure classification success rates, with the test set being independent of the training set. The error rate on the training set is optimistic. Cross validation techniques like 10-fold stratified cross validation are used when data is limited. - True success rates are predicted using properties of statistics and normal distributions. Confidence levels determine the range within which the true rate is expected to lie. - Techniques like paired t-tests are used to statistically compare the performance of different algorithms or data mining methods. They determine if performance differences are statistically significant.