The Ultimate Guide to Multiclass A/B Testing

The Ultimate Guide to Multiclass A/B Testing

Tackle any more than two classes A/B testing using these intuitive two steps

One essential skill that certainly useful for any data analytics professional to comprehend is the ability to perform an A/B testing and gather conclusions accordingly.

Before we proceed further, it might be useful to have a quick refresher on the definition of A/B testing in the first place. As the name suggests, we can think of A/B testing as the act of testing two alternatives, A and B, and use the test result to choose which alternative is superior to the other. For convenience, let’s call this type of A/B testing as the binary A/B testing.

Despite its name, A/B testing in fact can be made more general, i.e. to include more than two alternatives/classes to be tested. To name a few, analyzing click-through rate (CTR) from a multisegment digital campaign and redemption rate of various tiers of promos are two nice examples of such multiclass A/B testing.

The difference in the number of classes involved between binary and multiclass A / B testing also results in a slight difference in the statistical methods used to draw conclusions from them. While in binary testings one would straightforwardly use a simple t-test, it turns out that an additional (preliminary) step is needed for their multiclass counterparts.

In this post, I will give one possible strategy to deal with (gather conclusions from) multiclass A/B testings. I will demonstrate the step-by-step process through a concrete example so you can follow along. Are you ready?

hypothesis-testing a-b-testing click-through-rate t-test chi-square-test testing

What is Geek Coin

What is GeekCash, Geek Token

Best Visual Studio Code Themes of 2021

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

A/B Testing with Chi-Squared Test to Maximize Conversions and CTRs

A/B Testing with Chi-Squared Test to Maximize Conversions and CTRs. Arguably one of the most practical data science concepts in the workplace is A/B Testing. Let's a particular A/B Testing method that works well for comparing click-through rates and conversions.

How to Run the Chi-Square Test in Python

Example of Chi-Square Test in Python. We will provide a practical example of how we can run a Chi-Square Test in Python.

Hypothesis Tests Series-Post 1: T-test

Statistics play an important role in our lives. It eventually becomes an imperative fundamental of any data scientist. Today we are about…

Introduction to Hypothesis Test (Part One)

Hypothesis test is one of the most important domain in statistics, and in industry, ‘AB Test’ utilizes this idea as well. However, most of

Software Testing 101: Regression Tests, Unit Tests, Integration Tests

How do scientists demonstrate that a drug or vaccine is effective? Putting it to the test. It's your only choice. And, if you're reasonable, you'd never use medications that haven't been thoroughly tested. So, why don't you test software as thoroughly as you should?