Robust tests are tests that can handle the inclusion into a data file of some outliers without largely changing the overall test results. Despite the risk of non-Gaussian data in clinical trials, robust tests are virtually never performed. The objective of this study was to review important robust tests and to assess whether they provide better sensitivity of testing than standard tests do. In a 33 patient study of frailty scores, no significant t value was obtained (P = 0.067). The following 4 robust tests were performed: (1) z test for medians and median absolute deviations, (2) z test for Winsorized variances, (3) Mood test, and (4) z test for M-estimators with bootstrap standard errors. They produced P values of, respectively, <0.0001, 0.043, <0.0001, and 0.005. Robust tests are wonderful for imperfect clinical data because they often produce statistically significant results, whereas standard tests do not.