Mastering A/B testing for Data Science Interviews: Introduction to A/B Testing

A/B testing is a powerful and widely used methodology in the field of data science, particularly in the realm of product development and user experience optimization. It offers a structured approach to test and compare two different versions of a product or feature to determine which one yields better results. 

In this series of articles, we will cover everything you need to know to be able to master A/B testing for data science and data analysis interviews. We will start with an introduction to A/B testing in which we will delve into the fundamentals of A/B testing, its historical roots, practical case studies, its limitations, and alternative techniques to complement its findings.

Table of Contents:

1. What is A/B Testing?

2. History of A/B Testing

3. Should You Use A/B Testing for Every Change?

4. A/B Testing Practical Case Studies

5. What Are Things You Can’t Do With A/B Testing?

6. Other Techniques