Presence–absence data can be useful to wildlife managers in a wide variety of contexts, from monitoring populations at large spatial scales to identifying habitats that are of high value to specific species of conservation concern. However, a key issue is that a species may be declared “absent” from a landscape unit simply as a result of not detecting the species using the prescribed sampling methods. The effect of this imperfect detection is that parameter estimates will be biased, and any modeling of the data provides a description of the surveyors' ability to find the species on the landscape, not where the species is on the landscape. The reliability of so-called “presence–absence” data for making sound management decisions and valid scientific conclusions could therefore be questioned. However, after collecting appropriate data (i.e., repeated surveys of landscape units within a relatively short timeframe), recently developed statistical models can be used to obtain unbiased parameter estimates. Here I provide a nontechnical overview of the issues that pertain to wildlife studies or monitoring programs that seek to make reliable inference about the presence or absence of a target species.