How do you balance the trade-off between statistical significance and business relevance in A/B testing?

1 What is statistical significance?

Statistical significance is a measure of how confident you are that the difference between two variations is not due to chance. It is usually expressed as a p-value, which is the probability of observing the same or more extreme results if there was no real difference. The lower the p-value, the higher the statistical significance. A common threshold for statistical significance is 0.05, which means that there is only a 5% chance of a false positive.

2 What is business relevance?

Business relevance is a measure of how meaningful and impactful the difference between two variations is for your bottom line. It is usually expressed as a metric, such as revenue, conversion rate, or customer satisfaction. The higher the metric, the higher the business relevance. A common way to measure business relevance is to calculate the minimum detectable effect (MDE), which is the smallest difference that would make a significant change in your metric.

3 Why do you need to balance them?

You need to balance statistical significance and business relevance because they have opposite effects on the duration and validity of your A/B test. If you aim for high statistical significance, you need to run your test longer and collect more data, which increases the risk of external factors influencing your results, such as seasonality, competition, or user behavior changes. If you aim for high business relevance, you need to run your test shorter and collect less data, which increases the risk of internal factors influencing your results, such as random noise, sampling error, or measurement error.

4 How do you find the optimal trade-off?

Finding the optimal trade-off between statistical significance and business relevance is not a one-size-fits-all solution, but rather dependent on your context, objectives, and resources. To make an informed decision, you should first define your primary metric and MDE based on your business goals and expectations. Then, calculate your sample size and duration based on the metric, MDE, and desired statistical significance level. Monitor your test results to ensure they meet criteria for both statistical significance and business relevance. If they do, end the test and implement the winning variation. If they don't, evaluate the trade-offs and risks of continuing or stopping the test. Additionally, consider other factors such as ethical implications, user feedback, or opportunity costs that may influence your decision.

6 How do you improve your skills?

Balancing statistical significance and business relevance in A/B testing is not easy, requiring a combination of analytical, technical, and business skills. To improve these skills, you should learn the basics of statistics and the tools and platforms used for A/B testing. Additionally, you can learn from experts and peers with experience in A/B testing through blogs, podcasts, books, or courses. Practicing your skills by running your own A/B tests or analyzing case studies from other e-commerce retailers can also help. Finally, experiment with different variations, metrics, MDEs, and significance levels to better understand their effects on your results and decisions.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?