Data collection from online platforms, such as Mechanical Turk (MTurk), has become popular in clinical research. However, there are also concerns about the representativeness and the quality of this data for clinical studies. The present work explores these issues in the specific case of major depression. Analyses of two large data sets gathered from MTurk (N1 = 2,692 and N2 = 2,354) revealed two major findings: First, failing to screen for inattentive and fake respondents inflates the rates of major depression artificially and significantly (to 18.5% to 27.5%). Second, after cleaning the data sets, depression in MTurk is still 1.6 to 3.6 times higher than general population estimates. Approximately half of this difference can be attributed to differences in the composition of MTurk samples and the general population (i.e., socio-demographics, health and physical activity lifestyle). Several explanations for the other half are proposed and practical data-quality tools are provided.