Greenland first documented (Am J Epidemiol 1980; 112:564-9) that error in the measurement of a confounder could resonate--that it could bias estimates of other study variables, and that the bias could persist even with statistical adjustment for the confounder as measured. An important question is raised by this finding: can such bias be more than trivial within the bounds of realistic data configurations? The authors examine several situations involving dichotomous and continuous data in which a confounder and a null variable are measured with error, and they assess the extent of resultant bias in estimates of the effect of the null variable. They show that, with continuous variables, measurement error amounting to 40% of observed variance in the confounder could cause the observed impact of the null study variable to appear to alter risk by as much as 30%. Similarly, they show, with dichotomous independent variables, that 15% measurement error in the form of misclassification could lead the null study variable to appear to alter risk by as much as 50%. Such bias would result only from strong confounding. Measurement error would obscure the evidence that strong confounding is a likely problem. These results support the need for every epidemiologic inquiry to include evaluations of measurement error in each variable considered.