Uncertainty in assessment of individual exposure levels leads to bias, often, but not always, toward the null in estimates of health effects, and to underestimation of the variability of the estimates, leading to anticonservative p-values. In the absence of data on the uncertainty in individual exposure estimates, sensitivity analysis, also known as uncertainty analysis and bias analysis, is available. Hypothesized values of key parameters of the model relating the observed exposure to the true exposure are used to assess the resulting amount of bias in point and interval estimates. In general, the relative risk estimates can vary from zero to infinity as the hypothesized values of key parameters of the measurement error model vary. Thus, we recommend that exposure validation data be used to empirically adjust point and interval estimates of health effects for measurement error. The remainder of this review gives an overview of available methods for doing so. Just as we routinely adjust for confounding, we can and should routinely adjust for measurement error.