Surveys that record the presence or absence of fauna are used widely in wildlife management and research. A false absence occurs when an observer fails to record a resident species. There is a growing appreciation of the importance of false absences in wildlife surveys and its influence on impact assessment, monitoring, habitat analyses, and population modeling. Very few studies explicitly quantify the rate of these errors. Quantifying the rate of false absences provides a basis for estimating the survey effort necessary to assert that a species is absent with a pre-specified degree of confidence and allows uncertainty arising from false absences to be incorporated in inference. We estimated the rate of false absences for 2 species of forest owl and 4 species of arboreal marsupial based on 8 repeat visits to 50 survey locations in south-eastern Australia. We obtained estimates using a generalized zero-inflated binomial model. We presented detectability curves for each species to convey the number of visits required to achieve a specified level of confidence that resident species will be detected. The observation error rates we calculated were substantial but varied between species. For the least detectable species, the powerful owl (Ninox strenua), our standard surveys returned false absences on 87% of visits. However, our surveys of the more detectable sugar glider (Petaurus breviceps) returned a 45% false absence rate. We predict that approximately 18 visits would be required to be 90% sure of detecting resident owls and approximately 5 visits would provide 90% confidence of detecting resident sugar gliders. We fitted hierarchical logistic regression models to the data to describe the variation in detection rates explained by environmental variables. We found that temperature, rainfall, and habitat quality influenced the detectability of most species. Consideration of observation error rates could result in important changes to resource management and conservation planning.