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False positives and false negatives
In medical testing, and more generally in binary classification, a false positive is an error in data reporting in which a test result improperly indicates presence of a condition, such as a disease (the result is ''positive''), when in reality it is not, while a false negative is an error in which a test result improperly indicates no presence of a condition (the result is ''negative''), when in reality it is present. These are the two kinds of errors in a binary test (and are contrasted with a correct result, either a or a .) They are also known in medicine as a false positive (respectively negative) diagnosis, and in statistical classification as a false positive (respectively negative) error.
In statistical hypothesis testing the analogous concepts are known as type I and type II errors, where a positive result corresponds to rejecting the null hypothesis, and a negative result corresponds to not rejecting the null hypothesis. The terms are often used interchangeably, but there are differences in detail and interpretation due to the differences between medical testing and statistical hypothesis testing.
==False positive error==
A false positive error, or in short false positive, commonly called a "false alarm", is a result that indicates a given condition has been fulfilled, when it actually has not been fulfilled. I.e. erroneously a positive effect has been assumed. In the case of "crying wolf" – the condition tested for was "is there a wolf near the herd?", the actual result was that there had not been a wolf near the herd. The shepherd wrongly indicated there was one, by calling "Wolf, wolf!".
A false positive error is a type I error where the test is checking a single condition, and results in an affirmative or negative decision usually designated as "true or false".
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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