The article corrects tests for covariance matrices when the sample size is small compared to the dimension. The researchers propose adjustments to these tests to handle high-dimensional effects. Simulations show that the corrected tests work well for both moderate and high dimensions, while traditional tests fail. The corrections also work for non-Gaussian populations.