Monte Carlo methods are used to solve complex, high-dimensional problems, such as the pricing of financial derivatives. In order to improve the accuracy of Monte Carlo methods one sometimes replaces pseudo-random points with evenly-distributed deterministic points. Such methods are called quasi-Monte Carlo methods. However, deterministic methods are inherently biased, so there have been attempts to randomize quasi-Monte Carlo methods. This talks surveys recent results by the author and others and attempts to explain when one may expect randomized quasi-Monte Carlo methods to outperform traditional Monte Carlo methods.