False negative is identifying someone as non-dangerous when they in fact go on to commit a dangerous act. When trying to identify dangerous offenders (or other things as well), researchers often make mistakes. One of these mistakes is known as a False Positive. The false negative error is identifying someone as dangerous when they are not dangerous.
The concept of Bayes' theorem is that true rates of false positives and false negatives are not a function of the accuracy of the test alone, but also the actual rate or frequency of occurrence within the test population.
Type II errors (false negative): the error of accepting something that should have been rejected; e.g., such as finding a guilty person innocent.
A false negative occurs when a spam email is not detected as spam, but is classified as "non-spam".
A low number of false negatives is an indicator of the efficiency of "spam filtering" methods.