Selective sampling, a form of active learning, reduces the cost of labeling training data by asking only for the labels of the most informative unlabeled examples. We introduce a novel approach to selective sampling which we call co-testing. Co-testing can be applied to problems with redundant views (i.e., problems with multiple disjoint sets of attributes that can be used for learning). We analyze the most general algorithm in the co-testing family, naive co-testing, which can be used with virtually any type of learner. Naive co-testing simply selects at random an example on which the existing views disagree. We applied our algorithm to a variety of domains, including three real-world problems: wrapper induction, Web page classification, and discourse trees parsing. The empirical results show that besides reducing the number of labeled examples, naive co-testing may also boost the classification accuracy.