Uncertainties in the input variables are inevitable in any design process. As a consequence, the output responses are also uncertain. Robust design is one of the sought after approach to treat such uncertainties for controlling the variation in the output responses, while maximizing the mean performance. Variation is modeled by a measure of data spread. Often, the details of the uncertainties in the input space are not available readily and they are usually characterized from scarce sample realizations. In addition, there could also be outliers in the realizations. These will increase the error in the measure of spread of the output response. Hence, it is desirable that an approach that is insensitive to outliers but can characterize the spread of data is developed for robust design. In this work we propose using L moments to model the spread of data. The classical robust design formulation is reformulated using the second L moment (l2). The proposed approach is demonstrated on a turbine disk design with 17 design and random variables. The details of the uncertainties are not known. A DoE of 200 samples is used and at each DoE point, we propagate the uncertainties using scarce samples, which include outliers. Robust design is performed and it is shown that the proposed approach works better than the classical robust design formulation. Copyright © 2018 ASME.