seantauber / bayesian-a-b-tutorial

Bayesian A/B Testing Tutorial

Overview

This repository contains a Jupyter notebook tutorial on Bayesian A/B Testing, designed to provide a hands-on guide for understanding and implementing Bayesian methods in A/B testing scenarios. The tutorial is adapted from "Bayesian Methods for Hackers" by Cameron Davidson-Pilon, a seminal work that offers a practical introduction to Bayesian modeling using Python.

What is A/B Testing?

A/B testing is a statistical method used to compare two or more variants of a webpage, email, or other marketing asset to determine which one performs better on a given conversion goal. Traditionally, A/B testing has been performed using frequentist statistics to determine if there is a significant difference between the variants.

Bayesian Approach to A/B Testing

The Bayesian approach to A/B testing provides a probabilistic perspective, offering deeper insights into the uncertainty and variability of the data. Instead of merely deciding which variant is better, Bayesian methods allow us to calculate the probability that one variant is better than another by a certain degree, providing a more nuanced understanding of the test results.

Tutorial Notebook

Explore the tutorial through the Jupyter notebook available in this repository:

• Bayesian Modeling Workshop

This notebook includes detailed examples and code snippets to help you grasp the fundamentals of Bayesian statistics applied to A/B testing. It covers:

• Setting up a Bayesian model
• Interpreting posterior distributions
• Making informed decisions based on Bayesian computations

Installation and Setup

To run the notebook locally, ensure you have Jupyter installed and follow these steps:

1. Clone this repository: git clone https://github.com/seantauber/Bayesian-A-B-Tutorial.git
2. Navigate to the cloned directory and install required Python packages: pip install -r requirements.txt
3. Launch Jupyter Notebook: jupyter notebook

Contributing

Feedback and contributions to this tutorial are welcome. Feel free to fork the repository, make improvements, and submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Thank you for your interest in Bayesian A/B Testing!