Advances in sequencing technology have made it feasible to sequence patient-derived viral samples at a level sufficient for detection of rare mutations. These high-throughput, cost-effective methods are revolutionizing the study of within-host viral diversity. However, these techniques are error prone, and the methods commonly used to control for these errors have not been validated under the conditions that characterize patient-derived samples. In our study we showed that these conditions affect measurements of viral diversity. We found that the accuracy of previously benchmarked analysis pipelines were greatly reduced under patient-derived conditions. By sequencing known mixtures of single mutants we were able to identify biases in our method and improve our accuracy to acceptable levels. Here, we provide a "light", interactive version of the main figure so others may explore the impact different quality thresholds have on the accuracy of the data. A fuller shiny application can be downloaded below and provides more options for variant identification.