The use of non-invasive DNA-based sampling is becoming increasingly popular. However, the misidentification of individuals due to genotyping error (primarily allelic dropout) is a critical problem, especially when using individuals in the capture–mark–recapture (CMR) approach to estimate population size. We propose 2 simple and cost-effective tests, Examining Bimodality (EB) and Difference in Capture History (DCH), to determine whether a sample contains genotyping errors and the relative magnitude of the problem. These tests formalize currently used approaches for identifying genotyping errors. We evaluate the efficacy of these tests using simulated CMR data. Results show that both tests are effective at detecting genotyping errors and provide a strong indication of whether the data are error free. The EB and DCH tests apply to data in which multiple samples are associated with individuals, such as those generated by CMR sampling. Managers need to be able to identify and eliminate genotyping errors to produce population estimates that are both unbiased and scientifically defensible.