In this video, we show a hands-on demonstration of A/B testing using real data from Kaggle! We'll walk you through a comprehensive exploration of A/B testing using a dataset that examines the conversion status when a test group is shown an ad versus a general public announcement.

Our journey begins with a careful examination of the dataset, which includes variables such as the day of the week with the most ad impressions, the time of day with the most ad impressions, and the total number of ads shown to prospects. We methodically link each variable to conversion status and create insightful visualizations such as stacked bar charts, pie charts, and box plots to uncover meaningful patterns and trends.

After completing our exploratory analysis, we turn to the core of A/B testing – statistical hypothesis testing. We conduct a proper chi-square dependence test to determine the relationship between categorical variables and conversion status. In addition, we conduct a Mann-Whitney U test to compare the distributions of a continuous variable between the two groups, ensuring robust statistical validation of our results.

This video will help you master the art and science of A/B testing as we bridge the gap between theory and practice and empower you to use data-driven insights to make impactful decisions!

Have fun with your studying!

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