Lack of bias in the estimation of relative effect in epidemiologic studies depends on the internal validity of the study. This paper conveys in graphic and tabular form the direction and magnitude of bias due to misclassification of study subjects. A series of computer-generated graphs shows that the departure of the estimate of effect (relative risk or odds ratio) from its true value is a function of sensitivity and specificity (measures of classification validity), disease frequency, and exposure frequency. The discussion of bias emphasizes misclassification of the "outcome" variable; i.e., disease occurrence in a cohort study and exposure rate in a case-control study. Examples are used to illustrate that the magnitude of the bias can be large under circumstances which occur readily in epidemiologic research. When misclassification is equal for the two compared groups, the estimate is biased toward the null value, and in some instances beyond; when differential misclassification occurs (as in selective recall in case-control studies) the bias can be in either direction, and may be great. Formulas are derived to estimate the underlying true value of the relative risk or odds ratio using the investigator's observations together with the estimated sensitivity and specificity of the classification procedure.