Response:  This depends on the situation of the context and the data and domain that we are considering and trying to solve.  For email spam filtering use case, a false positive occurs when spam filtering or blocking techniques incorrectly classify a legitimate email message as spam. While most anti-spam techniques can block a high percentage of unwanted emails, doing so without creating significant false-positive outcomes is a much more demanding activity. Hence, we prefer too many false negatives over many false positives.  In another example of a medical testing scenario, false negatives may provide a falsely reassuring message to patients and physicians that disease is absent when it is present. This sometimes leads to inappropriate treatment of both the patient and their associated disease. Hence, it is desired to have too many false positives in this context.