A false positive is an error in some evaluation process in which a condition tested for is mistakenly found to have been detected.
In spam filters, for example, a false positive is a legitimate message mistakenly marked as UBE --unsolicited bulk email, as junk email is more formally known. Messages that are determined to be spam -- whether correctly or incorrectly -- may be rejected by a server or client-side spam filter and returned to the sender as bounce e-mail.
One problem with many spam filtering tools is that if they are configured stringently enough to be effective, there is a fairly high chance of getting false positives. The risk of accidentally blocking an important message has been enough to deter many companies from implementing any anti-spam measures at all.
False positives are also common in security systems. A host intrusion prevention system (HIPS), for example, looks for anomalies, such as deviations in bandwidth, protocols and ports. When activity varies outside of an acceptable range – for example, a remote application attempting to open a normally closed port -- an intrusion may be in progress. However, an anomaly, such as a sudden spike in bandwidth use, does not guarantee an actual attack, so this approach amounts to an educated guess and the chance for false positives can be high.
False positives contrast with false negatives, which are results indicating mistakenly that some condition tested for is absent.