# A/B Testing

> Statistical comparison of model variants to determine superior performance.

- **Category**: Data Science
- **Subcategory**: Evaluation
- **Canonical URL**: https://designpattern.fyi/patterns/ab-testing/

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## Description
**Context**: Comparing model versions requires rigorous statistical testing. A/B testing exposes different models to user segments and measures outcomes.


## Use Cases
Comparing model variants in production to determine which performs better on business metrics.



## Implementation Example

```python
# A/B Testing Pattern import scipy.stats as stats
def ab_test(conversion_a, conversion_b): # Perform t-test to compare conversion rates t_stat, p_value = stats.ttest_ind(conversion_a, conversion_b)
if p_value < 0.05: return "Statistically significant difference" else: return "No significant difference"
# Example usage model_a_conversions = [1, 0, 1, 1, 0, 1, 1, 0, 1, 1] model_b_conversions = [1, 1, 1, 1, 1, 0, 1, 1, 1, 1]
result = ab_test(model_a_conversions, model_b_conversions)
```



## Trade-offs


### Advantages

- - Statistical rigor

- - Real-world validation

- - Business alignment

- - Incremental rollout




### Considerations & Drawbacks

- - Long duration

- - Complex setup

- - Statistical power requirements

- - Ethical considerations







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**Reference**: [Original Source](https://www.optimizely.com/optimization-glossary/ab-testing/)