Many traditional statistical approaches to data analysis assume a relatively simple situation in which the investigator is testing a single hypothesis. Most research in psychiatry, on the other hand, is exploratory in nature and involves testing many hypotheses. Exploratory research presents special problems in data analysis, which are discussed in this overview. Special statistical approaches that are available to reduce error risk, such as the Bonferroni inequality, are described. The importance of selecting confidence levels appropriate to a particular investigation, rather than arbitrary use of the .05 level, is also discussed.