False negatives vs false positives? When is either one worse than the other?
False positive: A false positive error, or in short a false positive, commonly called a ""false alarm"", is a result that indicates a given condition exists, when it does not. For example, in the case of ""The Boy Who Cried Wolf"", the condition tested for was ""is there a wolf near the herd?""; the shepherd at first 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 wrongly gives an affirmative (positive) decision. However it is important to distinguish between the type 1 error rate and the probability of a positive result being false. The latter is known as the false positive risk (see Ambiguity in the definition of false positive rate, below).
False negative: A false negative error, or in short a false negative, is a test result that indicates that a condition does not hold, while in fact it does. In other words, erroneously, no effect has been inferred. An example for a false negative is a test indicating that a woman is not pregnant whereas she is actually pregnant. Another example is a truly guilty prisoner who is acquitted of a crime. The condition ""the prisoner is guilty"" holds (the prisoner is indeed guilty). But the test (a trial in a court of law) failed to realize this condition, and wrongly decided that the prisoner was not guilty, falsely concluding a negative about the condition. It depends, must answer why it depends.