Environmental DNA (eDNA) and metabarcoding are boosting our ability to acquire data on species distribution in a variety of ecosystems. Nevertheless, as most of sampling approaches, eDNA is not perfect. It can fail to detect species that are actually present, and even false positives are possible: a species may be apparently detected in areas where it is actually absent. Controlling false positives remains a main challenge for eDNA analyses: in this issue of Molecular Ecology Resources, Lahoz‐Monfort et al. () test the performance of multiple statistical modelling approaches to estimate the rate of detection and false positives from eDNA data. Here, we discuss the importance of controlling for false detection from early steps of eDNA analyses (laboratory, bioinformatics), to improve the quality of results and allow an efficient use of the site occupancy‐detection modelling (SODM) framework for limiting false presences in eDNA analysis.