Unlocking the Power of A/B Testing

A hands-on tutorial with Python implementation

Photo on ucompares.com

Table of contents

• The basics: What you need to know
• Key concepts
• Get started with the implementation
• Performing t-test
• Analyzing the test results
• Drawing conclusions from the test results
• Best practices for conducting reliable A/B tests
• Common pitfalls when conducting A/B tests
• Putting it all together

The implementation is available on my GitHub:

Portfolio/stats/ab-test.ipynb at main · smortezah/Portfolio

The basics: What you need to know

Let’s begin by discussing the basics of A/B testing. A/B testing is a statistical method used to compare two versions of a webpage, app, or any other user experience in order to determine which version performs better in terms of achieving a desired outcome, such as increasing conversion rates or click-through rates.