Data mining is on the interface of Computer Science and Statistics, utilizing advances in both disciplines to make progress in extracting information from large databases. It is an emerging field that has attracted much attention in a very short period of time. This article highlights some statistical themes and lessons that are directly relevant to data mining and attempts to identify opportunities where close cooperation between the statistical and computational communities might reasonably provide synergy for further progress in data analysis.

Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-con dent inferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA) provides a coherent mechanism for accounting for this model uncertainty. Several methods for implementing BMA haverecently emerged. We discuss these methods and present anumber of examples. In these examples, BMA provides improved out-of-sample predictive performance. We also provide a catalogue of

this paper we have shown the consequences of the commonly used method for testing hypotheses about the common effect in combining estimates from several independent studies, experiments or centres of a multi-centre trial, where the occurence of a random interaction of response with centres or studies is included in the considerations. We recommend the use of the proposed alternative test procedure with the better approximating test distribution. Acknowledgement: Thanks are due to Kepher Makambi for assistance with the simulations.