False Positive

A test result that incorrectly indicates the presence of a condition—saying 'yes' when the true answer is 'no.' In medicine, a false positive means diagnosing a disease that isn't there. In business: flagging a legitimate transaction as fraud, identifying a good employee as underperforming, or concluding a marketing campaign works when the result was random noise. False positives are the cost of sensitive detection systems—the more aggressively you screen, the more false alarms you generate.

When to use it

When designing or evaluating any detection, screening, or classification system; when interpreting test results—medical, statistical, or business; when false alarms are eroding trust in a monitoring system; when deciding how aggressively to filter or screen.

How it can help

Every screening system you build—fraud detection, lead scoring, hiring filters, anomaly alerts—produces false positives. The question is always: what's the cost of a false positive vs. a false negative? In fraud detection, a false positive means annoying a good customer; a false negative means losing money. In hiring, a false positive means a bad hire; a false negative means missing a great candidate. Calibrating this trade-off is the core design challenge.