Complete guide to hands-on A/B Testing | A/B testing in Python | All that you need to know

🔬 In this video, we cover hands-on demonstration of A/B testing using real-world data from Kaggle! We'll guide you through a comprehensive exploration of A/B testing, using a dataset that examines the conversion status when an ad is displayed to a test group versus a general public service announcement.
📊 Our journey begins with a meticulous exploration of the dataset, which includes variables such as the day of the week with the highest ad displays, the hour of the day with the most ad displays, and the total number of ads shown to prospects. We'll methodically pair each variable with the conversion status, creating insightful visualizations like stacked bar charts, pie charts, and box plots to uncover meaningful patterns and trends.
🔍 With our exploratory analysis complete, we'll move on to the heart of A/B testing-statistical hypothesis testing. We'll perform a proper chi-squared test of dependence to assess the relationship between categorical variables and the conversion status. Additionally, we'll conduct a Mann-Whitney U test to compare the distributions of a continuous variable between the two groups, providing robust statistical validation to our findings.
🚀 This video will help you master the art and science of A/B testing as we bridge the gap between theory and practice, empowering you to leverage data-driven insights for impactful decision-making ! 📊
Happy Learning!

[@debershimitra3757]

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[@prosmartanalytics]

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[@oluwakoredealashe8787]

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[@kisholoymukherjee]

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[@prosmartanalytics]

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