Statistical considerations in learning from data

In this paper, we focus on statistics. Classical statistics and Bayesian statistics are both employed in data mining. Both have advantages but both also have severe limitations in this context. We point out some of these limitations as well as some of the advantages. The fact that we may need to take account of evidence both internal and external to the data set presents a difficulty for classical statistics. The need to incorporate an objective measure of reliability creates a difficulty for Bayesian statistics. We outline an approach to uncertainty that promises to capture the best of both worlds by incorporating both background knowledge and objectivity.