In the analysis of epidemiologic data in which exposure has been measured on a continuous scale, cutpoints can be defined to delineate categories or exposure can be modeled as a continuous covariate by assuming a special functional shape of the effect on disease status. Rules for classifying exposure into two or more categories range from a priori selection of cutpoints to data-oriented rules. The risk estimates may vary, however, with the choice of cutpoint. If the cutpoint selected is that for which the most impressive effect of exposure on outcome is observed, the final result must be qualified by adjustment. In this paper, the authors propose a method for adjusting results which are derived by varying the cutpoint on a specified selection interval. Adjustment is derived from the null distribution of the maximally selected test statistic. The method should be applied to correct p values if the cutpoint used to define different levels of exposure is selected in such a way that the measure of difference between two risk groups, such as the odds ratio or relative risk, is maximized. No method is yet available for adjusting the resulting risk estimate and the corresponding confidence limits. The authors illustrate the statistical method by applying it to data from a case-control study of the association between exposure to magnetic fields and risk of cancer in children which was conducted recently in Denmark.