A/B Testing — 6 Practical Steps of Implementation with Full Explanation

9 min readMay 10
  • What is A/B testing
  • When to apply A/B testing
  • How to implement A/B testing
  • How to interpret A/B testing result
  • How to determine the size of A/B testing groups
  • Common used statistical tests in A/B testing

What is A/B testing

A/B testing, also known as split testing, is a statistical experiment used to compare two different versions of a webpage, feature, or other elements to determine which one performs better in terms of a desired outcome. It is commonly used in marketing, user experience (UX) design, and product development to make data-driven decisions. In other words, you can show version A of a piece of marketing content to one half of your audience and version B to another. A/B testing helps marketers observe how one version of a piece of marketing content performs alongside another.

picture source

We can simply says A/B testing is a type of hypothesis test, worth to bring up the fundamental components of hypothesis testing in statistical analysis are the concepts of null hypothesis (H0) and alternative hypothesis (H1), But A/B testing is not limited to the H0 and H1 framework. A/B testing involves comparing two or more variants to determine which one performs better, but it may involve different types of hypotheses depending on the specific scenario.

When to apply A/B testing

The purpose of A/B testing is to gather empirical data and make data-driven decisions to optimize and improve digital experiences. Therefore, A/B testing is applicable in various domains and can be used whenever there is a need to compare and evaluate different options to make data-driven decisions and improve outcomes.

Here are some examples of A/B testing:

  1. Website or App Design: A/B testing can be used to compare different design elements, layouts, colors, or navigation options to determine which version leads to better user engagement, conversion rates, or user satisfaction.
  2. Content Testing: A/B testing can help in testing different variations of content, such as headlines, product descriptions…

Data science notes and Personal experiences | UCLA 2023'