All companies nowadays have websites and everyone strives to increase the Conversion Rates of potential customers to their website, so changes are made constantly to attract them, and as a result, A/B Testing is conducted after an experiment of the new change and comparing the results with the old design. Using Python programming language, Jupyter Notebook, Python Pandas, NumPy, Statsmodels, and Hypothesis Testing tools to know for sure if the changes are actually going to increase conversion rates or not, and if the change is significant or not, then you can decide whether to stick with the old design or launch the new one.