Robustness analysis in the structured singular value (μ) framework is a worst-case paradigm which may be conservative in cases where the exact coalescence of worst model in the set is unlikely to occur. Rather one may be interested in assessing the risk of unlikely events to occur. Traditionally this leads to a probabilistic approach to assessing risk and these questions have been approached using Monte Carlo methods of sampling the parameter space to approximate the probability distribution of the parameters. Probabilistic μ or probabilistic gain was formulated to provide a bridge between worst-case analysis techniques and probabilistic measures of rare events. This paper describes the application probabilistic gain metrics to the NASA Generic Transport Model (GTM) aircraft. Probabilistic μ analysis results are compared with worst-case and Monte Carlo analysis to highlight the potential benefits of combining worst-case analysis with traditional probabilistic methods.