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A false positive is the dismissal or rejection of a null hypothesis (a general or default position or assumption) when the hypothesis is true.
In computing, a very common example of a false positive occurs within programs used to filter spam. When legitimate messages are identified as illegitimate and possibly moved to a specially designated folder or deleted, that identification is a false positive.
This term is also known as a Type 1 error or an A error.
A false negative, also known as a Type 2 error or a B error, is defined as failing to
dismiss or reject a null hypothesis when in fact it is false.
In computing, spam filters that identify legitimate email messages as spam may sometimes send those emails back to the sender as bounced email. False positives often happen when users set unnecessarily high restrictions. Even sophisticated spam filters, such as ones using Bayesian filtering, encounter false positives. Therefore, some companies judge the risk of false positives to be too great and never install spam filters at all.
Another example of a false positive is when an anti-virus program finds a virus in a uinfected file.