Interactions among coinfecting parasites have the potential to alter host susceptibility to infection, the progression of disease and the efficacy of disease control measures. It is therefore essential to be able to accurately infer the occurrence and direction of such interactions from parasitological data. Due to logistical constraints, perturbation experiments are rarely undertaken to directly detect interactions, therefore a variety of approaches are commonly used to infer them from patterns of parasite association in observational data. However, the reliability of these various approaches is not known. We assess the ability of a range of standard analytical approaches to detect known interactions between infections of nematodes and intestinal coccidia (Eimeria) in natural small-mammal populations, as revealed by experimental perturbations. We show that correlation-based approaches are highly unreliable, often predicting strong and highly significant associations between nematodes and Eimeria in the opposite direction to the underlying interaction. The most reliable methods involved longitudinal analyses, in which the nematode infection status of individuals at one month is related to the infection status by Eimeria the next month. Even then, however, we suggest these approaches are only viable for certain types of infections and datasets. Overall we suggest that, in the absence of experimental approaches, careful consideration be given to the choice of statistical approach when attempting to infer interspecific interactions from observational data.