Free ebookPractical Bayesian Statistics for Real-World Decisions: From Intuition to Implementation

Course Description

Practical Bayesian Statistics for Real-World Decisions: From Intuition to Implementation is a free ebook course in Basic studies and Statistics that helps you turn uncertainty into clear, defensible choices. Instead of treating data analysis as a ritual of pass or fail outcomes, you will learn Bayesian thinking as a decision toolkit that connects what you believe today with what the evidence says tomorrow.

You will build intuition for priors, likelihood, and posteriors and learn how belief updating works in practice, including a simple beta binomial calculation that makes uncertainty tangible. From there, the course moves into decision-relevant probabilities and credible intervals so you can answer questions like what is the chance the conversion rate is above a target, or how confident you should be before changing a product or policy.

The ebook emphasizes practical Bayesian models for proportions and rates, Bayesian A B testing beyond p values, and experiment decision metrics such as probability of superiority, expected loss, and business thresholds. You will see how sequential learning and sensible stopping rules can be handled without statistical gymnastics, and how to choose a variant using expected loss tied to a cost model that mirrors real organizational tradeoffs.

To support real workflows, you will learn regression with uncertainty through predictive distributions and actionable forecasts, along with guidance on choosing priors that help rather than harm. Weakly informative priors, prior dominance under sparse data, and prior sensitivity analysis are explained with mini case studies so you can stress test assumptions before they stress your decisions. When data is limited across many groups, you will use hierarchical modeling and partial pooling to compare stores, cohorts, or class performance with shrinkage and quantified uncertainty.

You will also learn model checking and calibration techniques you can actually use, including posterior predictive checks, outlier handling, and ways to detect overfitting. Computation is introduced in plain language with MCMC and variational inference concepts, plus a practical way to read diagnostics like chains, convergence, and effective sample size. The course includes Python pseudocode patterns for Bayesian workflows and finishes with reporting skills for non technical stakeholders, decision memo templates, and ethical communication of uncertainty. Start the course now and learn to make better decisions with Bayesian statistics, from intuition to implementation.