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The increasing complexity and pace of ecological change requires natural resource managers to consider entire species assemblages. Acoustic recording units (ARUs) require minimal cost and effort to deploy and inform relative activity, or encounter rates, for multiple species simultaneously. ARU‐based surveys require post‐processing of the recordings via software algorithms that assign a species label to each recording. The automated classification process can result in cross‐species misidentifications that should be accounted for when employing statistical modelling for conservation decision‐making.Using simulation and ARU‐based detection counts from 17 bat species in British Columbia, Canada, we investigate three strategies for adjusting statistical inference for species misclassification: (a) 'coupling' ambiguous and unambiguous detections by validating a subset of survey events post‐hoc, (b) using a calibration dataset on the software algorithm's (in)accuracy for species identification or (c) specifying informative Bayesian priors on classification probabilities. We explore the impact of different Bayesian prior specifications for the classification probabilities on posterior estimation. We then consider how the quantity of data validated post‐hoc impacts model convergence and resulting inferences for bat species relative activity as related to nightly conditions and yearly site occupancy after accounting for site‐level environmental variables.Coupled methods resulted in less bias and uncertainty when estimating relative activity and species classification probabilities relative to calibration approaches. We found that species that were difficult‐to‐detect and those that were often inaccurately identified by the software required more validation effort than more easily detected and/or identified species.Our results suggest that, when possible, acoustic surveys should rely on coupled validated detection information to account for false‐positive detections, rather than uncoupled calibration datasets. However, if the assemblage of interest contains a large number of rarely detected or less prevalent species, an intractable amount of effort may be required, suggesting there are benefits to curating a calibration dataset that is representative of the observation process. Our findings provide insights into the practical challenges associated with statistical analyses of ARU data and possible analytical solutions to support reliable and cost‐effective decision‐making for wildlife conservation/management in the face of known sources of observation errors. [ABSTRACT FROM AUTHOR]