The analysis of screening experiments is often done in two stages, starting with factor selection via an analysis under a main effects model. The success of the first stage is influenced by three components: (1) main effect estimators’ variances and (2) bias, and (3) the estimate of the noise variance. Component (3) has only recently been given attention with design techniques that ensure an unbiased estimate of the noise variance. In this talk, I propose a design criterion based on expected confidence intervals of the first stage analysis that naturally balances all three components. To address model misspecification, I propose a constrained design criterion that measures the inflation of common model selection criteria for underspecified models. A general computer search algorithm is presented and a direct construction method is also proposed for common screening models.