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Non-invasive genetic sampling has become a favored tool to enumerate wildlife. Genetic errors, caused by poor quality samples, can lead to substantial biases in numerical estimates of individuals. We demonstrate how the computer program DROPOUT can detect amplification errors (false alleles and allelic dropout) in a black bear (Ursus americanus) dataset collected in 2003 from northern Idaho, USA, and detect scoring and other database errors (misreads, shifts in scoring, and transcription errors). Removing errors from our sample via computer techniques reduced our minimum number alive index from 187 to 146 bears and was less expensive than commonly used multi-tube approaches. We subsequently estimated gene flow between our 2 study areas (Purcell and Selkirk Mountains), which are separated by a large, open, agricultural valley. Gene flow data suggested that, although this valley was not a complete barrier to movement, its effects on population substructure were not inconsequential. We documented a low level of substructure (G^'_ST = 0.097) between study areas. Assignment tests confirmed this, as assignment to the population where the animal was captured was 74% for the Purcell Mountains and 89% for the Selkirk Mountains.