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Keep shoppingBayesian Statistics: Practical A/B Testing
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295 students
Bayesian Statistics: Practical A/B Testing
Learn Bayes Theorem, Bayesian inference, Thompson Sampling, credible intervals, and practical A/B testing
Created byRiad Almadani • 70,000+ Students
Last updated 5/2026
English
What you'll learn
- Understand Bayes' Theorem using intuitive visual explanations.
- Master the Prior, Likelihood, and Posterior framework.
- Build intuition for Bayesian Inference.
- Apply Bayesian Statistics to practical A/B Testing.
- Understand Credible Intervals and Predictive Distributions.
- Learn how Thompson Sampling, UCB1, and Epsilon-Greedy improve experimentation.
- Compare Bayesian methods with traditional Frequentist statistics.
- Apply Bayesian Decision Theory to business problems.
- Use Expected Loss to make better decisions under uncertainty.
- Develop a practical Bayesian mindset for data-driven decision making.
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Course content
6 sections • 17 lectures • 2h 39m total length
Requirements
- No prior knowledge of statistics or advanced math is required! This course starts from the very basics and builds up intuitively.
- A curious mind and a desire to make better, data-driven decisions in business and life.
- Access to a computer with an internet connection (we'll use free online tools for any practical exercises).
Description
Make Better Decisions with Bayesian Statistics
Are you tired of relying on p-values and traditional statistical tests that often fail to reflect real-world decision making?
Bayesian Statistics offers a modern approach to reasoning under uncertainty. Instead of asking whether something is statistically significant, Bayesian methods help you continuously update your beliefs as new evidence becomes available, making them ideal for business, marketing, product development, and experimentation.
This course is designed to make Bayesian Statistics simple, intuitive, and practical. Rather than overwhelming you with advanced mathematics, you'll build a deep conceptual understanding using visual explanations, real-world examples, and hands-on applications.
You'll learn our intuitive Baby Bayes Framework, a step-by-step approach that explains Bayesian thinking through Prior, Likelihood, and Posterior before introducing more advanced concepts. By understanding the intuition first, you'll be able to apply Bayesian methods with confidence instead of memorizing formulas.
Unlike many Bayesian courses that focus almost entirely on theory, this course emphasizes practical decision making. You'll explore how Bayesian methods improve A/B testing, adaptive experimentation, and business decisions through real examples that demonstrate how Bayesian thinking is applied in practice.
You'll also discover how adaptive algorithms such as Epsilon-Greedy, UCB1, and Thompson Sampling can outperform traditional A/B testing by continuously learning while an experiment is running. Instead of waiting until the end of an experiment, you'll learn how Bayesian methods allow smarter decisions throughout the entire testing process.
As you progress, you'll learn Bayesian Inference, Credible Intervals, Predictive Distributions, Conjugate Priors, and Bayesian Decision Theory. You'll understand how uncertainty can be measured, how future outcomes can be predicted, and how Expected Loss helps choose the best decision when every option carries some level of risk.
Throughout the course, every concept is explained through practical examples rather than abstract mathematical proofs, making Bayesian Statistics accessible even if you don't have an advanced mathematics background.
Who this course is for:
- Data Analysts
- Data Scientists
- Product Managers
- Marketing Professionals
- Business Analysts
- A/B Testing Practitioners
- Students learning Bayesian Statistics
- Anyone interested in making better decisions with data and uncertainty