A/B testing is a method to measure the consequence/success rate of the action taken to make a change in any project or product. A/B testing is often deployed in online settings, used to compare what setting of options leads to the best performance.
Here, I'm using the data I found in Kaggle to perform A/B testing using both R and Python. I thank the data owner and the people who have shared their codes on Kaggle.
Let's say we are testing whether the design changes made on a website improved its conversion rate. We have an old and new design which was viewed by two groups namely, Control who was shown the old design and Treatment group who was shown the new design.