Definition
A False Negative (Type II Error) occurs when a test indicates an absence when a condition is actually present. It is an error in binary classification, common in statistics and signal detection theory.
Why It Matters
The cost of being wrong is rarely symmetrical. A false negative can be catastrophic—for example, a medical test says a patient is healthy when they actually have a deadly disease, or a security system failing to detect an intruder. Understanding this error is essential to optimizing detection systems.
Core Concepts
- False Negative (Beta Error): A “missed signal.” (e.g., a medical test says a patient is healthy when they actually have a disease).
- Sensitivity: The ability of a test to correctly identify those with the condition (minimizing false negatives).
- The Trade-off: Increasing specificity usually decreases sensitivity (leading to more false negatives). The optimal balance depends on the cost of the error.