A Guide to A/B Testing as a Data Scientist

Dive into a comprehensive tutorial on A/B testing for data scientists. Learn how to define experiments, create datasets, and conduct hypothesis testing. Explore the issue of interpretability and delve into Bayesian testing, including constructing priors and posteriors. Discover how to work with continuous metrics like prices, understand pricing distributions, and apply Bayesian math. Follow along with coded examples and learn to interpret results for continuous metrics. Access the provided GitHub repository for hands-on practice and refer to recommended resources for deeper understanding of Bayesian mathematics and beta distributions.

Introduction
Experiment Definition
Create Dataset for A/B Test
Hypothesis Testing
Issue of Interpretability
Bayesian Testing: Constructing Prior
Bayesian Testing: Constructing Posterior
Introducing a Continuous Metric: Prices
Understand Pricing Distributions
Add prices to dataset
Bayesian Math
Coded Math
Construct Priors, Posteriors
Interpret Results for Continuous Metric