In this lecture, we review different forms of Variance Reduction Techniques (VRT's) for stochastic simulation, which attempt to re-design simulation experiments to control for sources of variance and thus increase statistical power when making an estimate with a small number of replications. We start with common random numbers (CRN's) and Control Variates. We then pivot to discussing Antithetic Variates. The goal was to also cover Importance Sampling, but due to time constraints that topic will be saved for the next lecture period.