Engineering design decisions often need to be made when complete knowledge of the system is not available. Getting such complete deterministic information may be impossible or impractical as the resources required to get the information may be unaffordable. The challenge then is to identify and construct an information-gathering strategy, one that can be expected to yield most reliable results in an efficient manner during design. In this context, an engineering model can be viewed as an information-gathering strategy, using which predictive information regarding the performance of a design can be acquired by means of mathematical simulation studies. Focusing on this issue, this paper presents the development of a Bayesian analysis based model selection strategy to understand and deal with the uncertainty inherent in engineering models. Specifically, this work offers a basis for assessing engineering models under conditions of uncertainty through a methodical generation of the required probabilities in a Bayesian tree format, and subsequently using it in the selection of the best analysis model from a design decision perspective. The selection of the best model is made under conditions of uncertainty by considering the models’ performance on each design attribute, and based on the payoffs resulting from the design outcomes. The modeling of a windshield-wiper arm is used to illustrate the application of the proposed methodology.