False Negative

A test result that incorrectly indicates the absence of a condition—saying 'no' when the true answer is 'yes.' In medicine, a false negative means missing a disease that's actually present. In business: failing to detect actual fraud, missing that a high-potential employee is struggling, or not recognizing a genuine market shift because your metrics didn't capture it. False negatives are the cost of specific detection systems—the more you optimize for avoiding false alarms, the more real problems you miss.

When to use it

When designing detection systems and choosing sensitivity thresholds; when missed opportunities are more costly than false alarms; when evaluating why a monitoring system failed to flag a real problem; when the base rate of the condition is low (making false negatives especially likely).

How it can help

The hidden cost of conservative decision-making. Every time you raise the bar to reduce false positives (false alarms), you increase false negatives (missed realities). In hiring: raising interview standards reduces bad hires but increases missed talent. In product: conservative feature gates reduce bugs but delay valuable features. The question is always: which error is more expensive? Usually, the cost of missing something real is greater than the cost of a false alarm.