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A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present). A false negative is the opposite of a false positive, where the test result incorrectly indicates the absence of a condition when it is really present. The false positive and false negative are two kinds of errors in a binary test. In medicine, these errors are called false positive diagnosis or false negative diagnosis. In statistics, these errors are called false positive error or false negative error.
A false positive is an error when a result indicates that a given condition exists when it does not, such as a pregnancy test. A false positive is a type 1 error where the test is checking a single condition and incorrectly gives an affirmative decision.
A false negative is an error when the result wrongly indicates that a decision does not hold. An example of this is when a pregnancy test says a woman is not pregnant when in reality she is, or when a person is acquitted of a crime but is still guilty.
Example of when false positive is more important than false negative
Medical conditions that say the patient is positive when in reality he is not can be an example of when a false positive is more…
