The distinction between epistemic and aleatory uncertainty can sometimes be useful for practical purposes. In principle, epistemic uncertainty is reducible by obtaining more or better information. However, the boundaries between both types of uncertainties are often blurred. Fortunately, it is often the case that not all sources of uncertainty have the same impact on the predictive performance of numerical models, so that there is considerable computational benefit from investigating potential simplification in the number of parameters that should be modelled as being stochastic. In order to determine which parameters are the most important in terms of their contribution to the output's uncertainty, probabilistic sensitivity analysis can be performed. We apply this idea to a numerical model of corrugated skins. Corrugated skins are particularly suitable for morphing applications in aerospace structures, largely due to the very high compliance they offer along the corrugation direction.