Overview

• A/B testing is a popular way to test your products and is gaining steam in the data science field
• Here, we’ll understand what A/B testing is and how you can leverage A/B testing in data science using Python

Introduction

Statistical analysis is our best tool for predicting outcomes we don’t know, using the information we know.

Picture this scenario — You have made certain changes to your website recently. Unfortunately, you have no way of knowing with full accuracy how the next 100,000 people who visit your website will behave. That is the information we cannot know today, and if we were to wait until those 100,000 people visited our site, it would be too late to optimize their experience.

This seems to a classic Catch-22 situation!

This is where a data scientist can take control. A data scientist collects and studies the data available to help optimize the website for a better consumer experience. And for this, it is imperative to know how to use various statistical tools, especially the concept of A/B Testing.

A/B Testing is a widely used concept in most industies nowadays, and data scientists are at the forefront of implementing it. In this article, I will explain A/B testing in-depth and how a data scientist can leverage it to suggest changes in a product.

#a-b-testing #machine-learning #data-science #python