The present thesis is a study of the robustness and performance of a test applicable in the high-dimensional context (𝑝>𝑛) whose components are unbiased statistics (U-statistics). This test (the U-test) has been shown to perform well under a variety of circumstances and can be adapted to any general linear hypothesis. However, the robustness of the test is largely unexplored. Here, a simulation study is performed, focusing particularly on violations of the assumptions the test is based on. For extended evaluation, the performance of the U-test is compared to its permutation counterpart. The simulations show that the U-test is robust, performing poorly only when the permutation test does so as well. It is also discussed that the U-test does not inevitably rest on the assumptions originally imposed on it.