1. Assessing the probability that a given site is occupied by a species of interest is important to resource managers, as well as metapopulation or landscape ecologists. Managers require accurate estimates of the state of the system, in order to make informed decisions. Models that yield estimates of occupancy, while accounting for imperfect detection, have proven useful by removing a potentially important source of bias. To account for detection probability, multiple independent searches per site for the species are required, under the assumption that the species is available for detection during each search of an occupied site. 2. We demonstrate that when multiple samples per site are defined by searching different locations within a site, absence of the species from a subset of these spatial subunits induces estimation bias when locations are exhaustively assessed or sampled without replacement. 3. We further demonstrate that this bias can be removed by choosing sampling locations with replacement, or if the species is highly mobile over a short period of time. 4. Resampling an existing data set does not mitigate bias due to exhaustive assessment of locations or sampling without replacement. 5. Synthesis and applications. Selecting sampling locations for presence/absence surveys with replacement is practical in most cases. Such an adjustment to field methods will prevent one source of bias, and therefore produce more robust statistical inferences about species occupancy. This will in turn permit managers to make resource decisions based on better knowledge of the state of the system.