The application of information reduction measures (IRMs) can provide valuable insight and enhance the process of design optimization when dealing with data uncertainty. For the engineering task of designing structures or products, an adequate modeling of data uncertainty is required. Therefore, a consideration of both aleatoric and epistemic uncertainty in combined form as polymorphic uncertain input variables is utilized. The resulting uncertain output quantities are post‐processed to provide relevant insights into robustness and performance for the design optimization. To this end, IRMs are applied, categorized into representative and uncertainty quantifying measures. Various IRMs exist, but clear recommendations or explanations of why certain uncertainty quantifying measures are chosen are scarce, although different features of uncertain quantities are considered with different measures. The aim of this contribution is to give an insight to commonly applied IRMs and the specific information of the uncertain quantity they reflect. Additionally, handling results of nested uncertainty analyses of polymorphic uncertain quantities regarding robustness towards aleatoric and epistemic uncertainty is investigated.