false positive
A false positive is a result in which a test or detection system wrongly reports a condition or problem that is not actually present.
A false positive occurs when a test, classifier or detection system flags something as matching a condition when it does not. In statistics it corresponds to a type I error. The opposite error, missing something that does match, is a false negative.
The concept is used in security vulnerability scanning, spam filtering, code analysis tools, medical testing and machine learning classification. Making a system more sensitive so that it catches more true cases usually lowers false negatives but raises false positives, so the two errors often have to be balanced.
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Articles covering this entry
Early cancer screening tests often produce ten false positives, results that flag a disease the person does not have, for every true positive.
…pursuit of one goal without measuring whether the agent can recognize a secure target or limit false positives—reports of issues that are not actually present.
A blocked call may reflect a useful protection or a false positive; the underlying cases need review before a developer changes a security setting.