Parentage studies and family reconstructions have become increasingly popular for investigating a range of evolutionary, ecological and behavioral processes in natural populations. However, a number of different assignment methods have emerged in common use, and the accuracy of each may differ in relation to the number of loci examined, allelic diversity, incomplete sampling of all candidate parents, and the presence of genotyping errors. Here we examine how these factors affect the accuracy of three popular parentage inference methods (COLONY, FaMoz and an exclusion-Bayes’ theorem approach by Christie et al. (2010a)) to resolve true parent-offspring pairs using simulated data. Our findings demonstrate that accuracy increases with the number and diversity of loci. These were clearly the most important factors in obtaining accurate assignments explaining 75-90% of variance in overall accuracy across 60 simulated scenarios. Furthermore, the proportion of candidate parents sampled had a small but significant impact on the susceptibility of each method to either false positive or false negative assignments. Within the range of values simulated, COLONY outperformed FaMoz, which outperformed the exclusion-Bayes’ theorem method. However, with 20 or more highly polymorphic loci, all methods could be applied with confidence. Our results show that for parentage inference in natural populations, careful consideration of the number and quality of markers will increase the accuracy of assignments and mitigate the effects of incomplete sampling of parental populations.