Many questions in quantitative finance, uncertainty quantification, and other disciplines are answered by computing the population mean, \(\mu := \mathbb {E}(Y)\) , where instances of \(Y:=f(\boldsymbol{X})\) may be generated by numerical simulation and \({\boldsymbol{X}}\) has a simple probability distribution. The population mean can be approximated by the sample mean, \(\hat{\mu }_n := n^{-1} \sum _{i=0}^{n-1} f({\boldsymbol{x}}_i)\) , for a well chosen sequence of nodes, \(\{{\boldsymbol{x}}_0, {\boldsymbol{x}}_1, \ldots \}\) , and a sufficiently large sample size, n. Computing \(\mu \) is equivalent to computing a d-dimensional integral, \(\int f({\boldsymbol{x}}) \varrho ({\boldsymbol{x}}) \, \textrm{d} {\boldsymbol{x}}\) , where \(\varrho \) is the probability density for \({\boldsymbol{X}}\) . Quasi-Monte Carlo methods replace independent and identically distributed sequences of random vector nodes, \(\{{\boldsymbol{x}}_i \}_{i = 0}^{\infty }\) , by low-discrepancy sequences. This accelerates the convergence of \(\hat{\mu }_n\) to \(\mu \) as \(n \rightarrow \infty \) . This tutorial describes low-discrepancy sequences and their quality measures. We demonstrate the performance gains possible with quasi-Monte Carlo methods. Moreover, we describe how to formulate problems to realize the greatest performance gains using quasi-Monte Carlo. We also briefly describe the use of quasi-Monte Carlo methods for problems beyond computing the mean, \(\mu \) .