A/B Testing Analysis for E-commerce Decision-Making

Objective: The aim is to analyze the results of an A/B test conducted by an e-commerce site and provide insights to help the company make decisions regarding the implementation of a new page versus retaining the old page.

Key Sections:

1. Part I - Probability:

   • Explore probability concepts and define null and alternative hypotheses.

2. Part II - A/B Test:

   • Consider duration and consistency in A/B test significance.
   • Conduct simulations and sampling distributions for decision-making.

3. Part III - Regression Approach:

   • Utilize logistic regression to analyze conversion rates.
   • Create dummy variables and fit regression models to determine page impact on conversions.

Conclusion: Inconclusive significance suggests retention of the old page may be preferable. The analysis includes data cleaning, statistics extraction, p-value calculation, and regression approach.

(Please note that detailed insights and specific analysis results are not provided in this summary.)

Check the full code on my GitHub repository

Sample from the analysis:

Part I - Cleaning data

Part II - Extracting statistics

I calculated the P-value, this huge value indicates that old page is better than the new page (alpha is .05) so we fail to reject the null hypothesis

Part III - A regression approach