# False Positive

**Category:** metrics  
**Short Description:** An error in data analysis where a test incorrectly indicates the presence of a condition or effect that is not actually present.  
**Last Updated:** 2025-03-12T00:00:00Z

## Definition

A false positive occurs when a test, algorithm, or detection system incorrectly identifies a positive result when the condition being tested for is not actually present. In marketing analytics, false positives can lead to incorrect conclusions about campaign performance, audience behavior, or anomaly detection, potentially resulting in misallocated resources or inappropriate optimization decisions.

## Calculation

**Formula:** `FPR = FP / (FP + TN)`

**Explanation:** False Positive Rate (FPR) measures the proportion of actual negatives incorrectly identified as positive. Lower values indicate better specificity and fewer false alarms.

### Components

- **False Positives**: Number of incorrect positive identifications
- **True Negatives**: Number of correct negative identifications

## Examples

- Incorrectly identifying a random performance spike as a successful campaign optimization
- Flagging normal seasonal fluctuations as anomalies requiring investigation
- Attribution models crediting conversions to ads that had no actual influence
- A/B test showing statistical significance when no real difference exists

## Best Practices

- Implement appropriate statistical thresholds based on risk tolerance
- Use control groups to validate findings
- Require multiple signals before taking significant actions
- Consider business context when interpreting statistical results
- Balance false positive and false negative risks appropriately

## Related Terms

### Similar Terms

- **[False Negative](/resources/glossary/metrics/false-negative)**: Failing to detect a condition that is actually present

### Component Terms

- **[Statistical Significance](/resources/glossary/metrics/statistical-significance)**: Confidence thresholds that influence false positive rates

### Child Terms

- **[Anomaly Detection](/resources/glossary/metrics/anomaly-detection)**: Systems that must balance false positive and negative risks

## Featured in topic hubs

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