Lack of independence, or pseudoreplication, in samples from ecological studies of insects reflects the complexity of working with living organisms: the finite and limited input of individuals, their relatedness (ecological and/or genetic), and the need to group organisms into functional experimental units to estimate population parameters (e.g., cohort replicates). Several decades ago, when the issue of pseudoreplication was first recognized, it was highlighted that mainstream statistical tools were unable to account for the lack of independence. For example, the variability as a result of differences across individuals would be confounded with that of the experimental units where they were observed (e.g., pans for mosquito larvae), whereas both sources of variability now can be separated using modern statistical techniques, such as the linear mixed effects model, that explicitly consider the different scales of variability in a dataset (e.g., mosquitoes and pans). However, the perception of pseudoreplication as a problem without solution remains. This study presents concepts to critically appraise pseudoreplication and the linear mixed effects model as a statistical solution for analyzing data with pseudoreplication, by separating the different sources of variability and thereby generating correct inferences from data gathered in studies with constraints in randomization.