Other than common random numbers, control variates is the most promising variance reduction technique in terms of its potential for widespread use: Control variates is applicable in single or multiple response simulation, it does not require altering the simulation run in any way, and any stochastic simulation contains potential control variates. A rich theory of control variates has been developed in recent years. Most of this theory assumes a specific probabilistic structure for the simulation output process, usually joint normality of the response and the control variates. When these assumptions are not satisfied, desirable properties of the estimator, such as unbiasedness, may be lost. A number of remedies for violations of the assumptions have been proposed, including jackknifing and splitting. However, there has been no systematic analytical and empirical evaluation of these remedies. This paper presents such an evaluation, including evaluation of the small-sample statistical properties of the proposed remedies.