Nonresponse weighting is a common method for handling unit nonresponse in surveys and is aimed at reducing nonresponse bias. Because the method can be accompanied by an increase in variance, the efficacy of weighting adjustments is often seen as a bias-variance trade-off. This view is an oversimplification, because weighting can reduce variance as well as bias. The authors provide a detailed analysis of bias and variance in setting weights to estimate a survey mean based on adjustment cells and suggest that the most important feature of variables for inclusion is that they are predictive of survey outcomes. Prediction of the propensity to respond is a secondary, though useful, goal. The authors also evaluate empirical estimates of root mean squared error for assessing when weighting is effective.