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The goal of A/B testing is to gather data to support decision making. While looking at the analytics dashboards and metrics, the most common question a data scientist, product manager or consultant encountered is: how could I translate the A/B testing results into a launch/no-launch decision? To properly answer this question, we need to take into consideration of both the conclusion drawn from the measurement data and the broader context, such as trades offs between metrics, various costs and risks.

In this article, we will only be focusing on the statistics behind designing an A/B test and interpreting the results…

Eva He

Data Science, Cloud Computing, ML, Quantum Computing | Fitness enthusiast | Avocado Lover 🥑

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