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This dissertation is concerned with the evaluation of a robust modification of existing methodology within the classical inference framework. This results in an F-test based on the robust weights used in arriving at the M or Bounded-Influence estimates. These estimates are known to be robust to outliers and highly influential points, respectively. The first part of this evaluation involves a Monte Carlo power study, under violations of the classical assumptions, of this F-test based on robust weights and several other proposed robust tests. It is shown in simulation studies that, under certain conditions, the F-test based on robust weights is a much more powerful test than the classical F -test, and compares favorably to all other proposals studied. The second part involves the development of the influence curve (IC) for the F-test based on robust weights and one empirical approximation to the IC, the Sample Influence Curve (SIC). It is shown for two sample data sets that the SIC demonstrates the resistance to unusual points of the F-test based on robust weights.