# False Negative

**Category:** metrics  
**Short Description:** An error where a test incorrectly indicates the absence of a condition when it is actually present.  
**Last Updated:** 2025-03-12T00:00:00Z

## Definition

A false negative occurs when a test, algorithm, or detection system fails to identify a condition or event that is actually present. In digital advertising, false negatives represent missed opportunities where the system fails to recognize valuable signals, such as potential conversions, fraud instances, or relevant audience segments. These errors can lead to underreporting of performance, missed optimization opportunities, and inefficient resource allocation.

## Calculation

**Formula:** `False Negative Rate = Missed Positives / Total Actual Positives`

**Explanation:** Measures the proportion of actual positive cases that were incorrectly classified as negative. Lower values indicate better detection accuracy.

### Components

- **Missed Positives**: Number of actual positive cases incorrectly classified as negative
- **Total Actual Positives**: Total number of actual positive cases

## Examples

- Attribution model failing to credit touchpoints that influenced conversions
- Fraud detection system missing bot traffic that should have been flagged
- Audience targeting algorithm excluding users who would have converted
- Anomaly detection failing to identify significant performance issues
- Conversion tracking missing valid conversions due to technical issues

## Best Practices

- Balance false negative and false positive rates based on business impact
- Implement multiple detection layers for critical systems
- Regularly validate detection accuracy with known test cases
- Adjust sensitivity thresholds based on performance requirements
- Consider the cost of missed detections when configuring systems

## Related Terms

### Opposite Terms

- **[False Positive](/resources/glossary/metrics/false-positive)**: Incorrect identification of a condition when it's actually absent

### Component Terms

- **[Statistical Significance](/resources/glossary/metrics/statistical-significance)**: Helps establish confidence levels that minimize false negatives
- **[Anomaly Detection](/resources/glossary/metrics/anomaly-detection)**: Systems must balance false negative and false positive rates
- **[A/B Testing](/resources/glossary/creative/ab-testing)**: False negatives can lead to rejecting effective variations

## Featured in topic hubs

- [Experimentation & Statistics](/resources/topics/experimentation)