Statisticians use simulation studies to evaluate the performance of statistical methods. Performance measures are what we use to evaluate performance; examples include bias and coverage. This talk will describe some of the key performance measures that can be used. Tim Morris will consider simulation studies of statistical methods that output a point estimate, standard error and (possibly) confidence interval. Tim will describe several performance measures by outlining what they aim to quantify, how they are calculated, and how Monte Carlo standard errors are estimated. Performance measures may use the (repeated) point estimates, the standard errors, the confidence intervals or more than one of these. Understanding performance measures and how they link together can help us to better critique simulation studies that we read and better plan our own. A key message is that it is rarely appropriate to evaluate only one performance measure.