A guide to A/B testing — How to formulate, design and interpret

A guide to A/B testing — How to formulate, design and interpret

In this article, you will find how to design a robust A/B test that gives you repeatable results, what are the main pitfalls of A/B testing that require additional attention and how to interpret the results.

The online world gives us a big opportunity to perform experiments and scientifically evaluate different ideas. Since these experiments are data-driven and providing no room for instincts or gut feelings, we can establish causal relationships between changes and their influence on user behavior. Leveraging on these experiments, many organizations can understand their customers’ liking and preferences by avoiding the so-called HiPPO effect😅

A/B testing is a common methodology to test new products or new features, especially regarding user interface, marketing and eCommerce. The main principle of an A/B test is to split users into two groups; showing the existing product or feature to the control group and the new product or feature to the *experiment group. *Finally, evaluating how users respond differently in two groups and deciding which version is better. Even though A/B testing is a common practice of online businesses, a lot can easily go wrong from setting up the experiment to interpreting the results correctly.

In this article, you will find how to design a robust A/B test that gives you repeatable results, what are the main pitfalls of A/B testing that require additional attention and how to interpret the results.

You can check out the Jupyter Notebook on my GitHub for the full analysis.

data-science testing statistics python a-b-testing

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