The International Union for Conservation of Nature (IUCN) Red List provides a globally-recognized evaluation of the conservation status of species, with the aim of catalysing appropriate conservation action. However, in some parts of the world, species data may be lacking or insufficient to predict risk status. If species with shared ecological or life history characteristics also tend to share their risk of extinction, then ecological or life history characteristics may be used to predict which species may be at risk, although perhaps not yet classified as such by the IUCN. Statistical models may be a means to determine whether there are non-threatened or unclassified species that share the characteristics of threatened species, however there are no data on which model might be most appropriate or whether multiple models should be used. In this paper, three types of statistical models, namely regression trees, logistic regression and discriminant function analysis are compared using data on the ecological characteristics of Finnish lepidopterans (butterflies and moths). Overall, logistic regression performed slightly better than discriminant function analysis in predicting species status, and both outperformed regression trees. Uncertainty in species classification suggests that multiple analyses should be performed and particular attention devoted to those species for which the methods disagree. Such standard statistical methods may be a valuable additional tool in assessing the likely threat status of a species where there is a paucity of abundance data.