Measurement systems analysis (MSA) studies are designed experiments that determine how much measurement variation is contributing to overall process variation. These studies are a critical first step in determining whether a measurement system will be able to detect process shifts with control charts or correctly identify product improvements in designed experiments. While typical MSA studies that are presented in guidebooks, college textbooks and journals have small sample sizes, they have been widely emulated despite the fact that there has been little published research on the sample sizes needed for these studies to give reliable results. We will show the results of a series of simulation experiments that investigate the relationship between sample size, estimation method (REML versus Bayesian), the actual quality of the measurement system, and the way the MSA study is collected (crossed versus nested). Based on the results, we recommend the sample sizes needed to correctly determine the adequacy of a measurement system with high probability. As part of the presentation, we will demonstrate how we used the new Simulate option in JMP Pro 13 to make the simulation experiments easy.