Analyzing A/B Test Results

of E-Commerce Website

Introduction

A/B tests are very commonly performed by data analysts and data scientists.

For this project, I will be working to understand the results of an A/B test run by an e-commerce website. The goal is to work through this notebook to help the company understand if they should implement the new page, keep the old page, or perhaps run the experiment longer to make their decision.

Methodology

Part I - Probability

Getting more familiar with the dataset, analyzing probabilities, and cleaning data.

Part II - A/B Test

Setting the hypothesis test based on the conversion rate of pages; old and new.

Part III - Regression

Achieve the same results of A/B testing by performing regression.

Conclusion

Based on the results of our analysis, we can't confirm any significant differance in conversion based on page, country, or even the interaction between them. It worth pointing out that the data is collected in a period of 22 days which may not be long enough for the results to be accurate and reflect the differences in conversion if exist.

In such case, it is recommended to keep experimenting for a longer period before making any decision.