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 when a test result improperly indicates no presence 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 . These are also known in medicine as a false positive diagnosis (resp. false negative diagnosis), and in statistical classification as a false positive error (resp. false 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.