Robust-design experiments can be a very useful tool for improving quality. They enable engineers to reduce the variance of important quality characteristics by identifying design factors with dispersion effects and guiding the choice of nominal levels of those factors. Robust-design experiments are especially effective when it is possible to build some variation directly into the experiment by including noise factors–factors that are impossible or too expensive to control during actual production or use. When noise factors are included, it is important to model their effects explicitly in the subsequent analysis. We present two examples in which failure to do so leads to incorrect conclusions about dispersion effects.