Several plausible modeling strategies are available to develop finite element (FE) models of ever-increasingly complex phenomena. Expert judgment is typically used to choose which strategy to employ, while the "best" modeling approach remains unknown. This paper proposes a decision analysis methodology that offers a systematic and rigorous methodology for comparing plausible modeling strategies. The proposed methodology departs from the conventional approach that considers only test-analysis correlation to select the model that provides the highest degree of fidelity-to-data. The novelty of the methodology lies in an exploration of the trade-offs between robustness to uncertainty and fidelity-to-data. Exploring robustness to model imprecision and inexactness, in addition to fidelity-to-data, lends credibility to the simulation by guaranteeing that its predictions can be trusted even if some of the modeling assumptions and input parameters are incorrect. To demonstrate this approach, an experimental configuration is analyzed in which large masses are used to load the CX-100 wind turbine blade in bending during vibration testing. Two plausible simulations are developed with differing strategies to implement these large masses using (i) a combination of point-mass and spring elements or (ii) solid elements. In this paper, the authors study the ability of the two FE models to predict the experimentally obtained natural frequencies, and the robustness of these competing models to uncertainties in the input parameters. Considering robustness for model selection provides the extent to which prediction accuracy deteriorates as the lack-of-knowledge increases. Therefore, the preferable modeling strategy is the one that offers the best compromise between fidelity-to-data and robustness to uncertainty. To predict the bending vibration of the CX-100 wind turbine blade, it is observed that the modeling strategy with solid elements is far superior to the other one in its ability to provide a compromise between fidelity-to-data and robustness to the modeling assumptions.