Evaluate A/B Test Results for New Feature

Business Context

E-commerce platform ShopSmart recently launched a new product recommendation feature aimed at increasing conversion rates. The feature was tested through an A/B test, where 10,000 users were exposed to the new feature (Group A) and 10,000 users continued using the old version (Group B). Over a two-week period, the conversion rate for Group A was 5.5% while Group B maintained a conversion rate of 4.8%. The product team is eager to understand whether this difference is statistically significant and if the new feature should be rolled out to all users.

Available Data

Task

1. Define the conversion rate metric and explain its relevance.
2. Calculate the conversion rates for both groups, including the number of conversions and total users.
3. Conduct a hypothesis test to determine if the difference in conversion rates is statistically significant. Include the null and alternative hypotheses.
4. Provide a confidence interval for the difference in conversion rates.
5. Recommend whether to roll out the new feature based on your findings and explain your reasoning.

Constraints

• The analysis must be completed within 48 hours for an upcoming product meeting.