Prospective epidemiological studies which seek to relate potential risk factors to the risk of disease are subject to appreciable biases which are often unrecognized. The inability to precisely measure subjects' true values of the risk factors under consideration tends to result in bias towards unity in the univariate relative risks associated with them--the more imprecisely a risk factor is measured, the greater the bias. When correlated risk factors are measured with different degrees of imprecision the adjusted relative risk associated with them can be biased towards or away from unity. When designing a new prospective study cost considerations usually limit the total number of subject-evaluations that are available. The usual design approach is to maximize the study size and evaluate each subject on one occasion only. An alternative approach involves recruitment of a smaller number of subjects so that each can be evaluated on more than one occasion, thus resulting in a more precise measure of subjects' risk factor values and hence less bias in the relative risk estimates. In this paper we use a simulation approach to show that under conditions that prevail for most major prospective epidemiological studies the latter approach is actually more likely to produce accurate relative risk estimates. This emphasizes the importance of bias due to exposure measurement imprecision and suggests that attempts to anticipate and control it be given at least as high a priority as that given to sample size assessment in the design of epidemiological studies.