A<unk> <unk> 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), while a<unk> <unk> is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a ' and a '). They are also known in medicine as a<unk> <unk> (or<unk> <unk>  diagnosis, and in statistical classification as a<unk> <unk> (or<unk> <unk>  error. In statistical hypothesis testing the analogous concepts are known as type I and type II errors, where a<unk> result corresponds to rejecting the null hypothesis, and a<unk> 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 positives and false negatives
Diagnostic overshadowing
Proportional reporting ratio
Dose–response relationship
Age bias 
Unwarranted variation
Construct validity
Automation bias