Burnette et al. aimed to validate two eating disorder symptom measures among transgender adults recruited from Mechanical Turk (MTurk). After identifying several data quality issues, Burnette et al. abandoned this aim and instead documented the issues they faced (e.g., demographic misrepresentation, repeat submissions, inconsistent responses across similar questions, failed attention checks). Consequently, Burnette et al. raised concerns about the use of MTurk for psychological research, particularly in an eating disorder context. However, we believe these claims are overstated because they arise from a single study not designed to test MTurk data quality. Further, despite claiming to go “above and beyond” current recommendations, Burnette et al. missed key screening procedures. In particular, they missed procedures known to prevent participants who use commercial data centers (i.e., server farms) to hide their true IP address and complete multiple surveys for financial gain. In this commentary, we outline key screening procedures that allow researchers to obtain quality MTurk data. We also highlight the importance of balancing efforts to increase data quality with efforts to maintain sample diversity. With appropriate screening procedures, which should be preregistered, MTurk remains a viable participant source that requires further validation in an eating disorder context.