I. Introduction
A/B testing, also known as split testing, is a method used in advertising and marketing to compare two versions of a variable, typically an advertisement, to determine which one performs better. The goal is to identify changes that can improve the overall effectiveness of a marketing campaign.
In this comprehensive project, you will delve into the world of A/B testing, a powerful statistical method widely employed in digital marketing. A/B testing allows marketers to assess the effectiveness of different strategies, such as testing a new ad against an existing one, to make informed decisions that can significantly impact campaign success.
Over the course of this project, you’ll learn how to explore and gain insights from your dataset, set up hypothesis testing, create a sampling distribution using bootstrapping, and finally, evaluate the null hypothesis to draw meaningful conclusions. By the end of this project, you’ll have the knowledge and practical experience to advise a digital marketing agency on whether to adopt a new ad based on statistical analysis.
Here is my notebook with full codes.
Let’s explore our dataset:
