StatsTest Blog
Experimental design, data analysis, and statistical tooling for modern teams. No hype, just the math.
Bayesian A/B Testing: Posterior Probabilities for Ship Decisions
How to run Bayesian A/B tests that give you the probability a variant wins. Practical guide with Python code for conversion rates and revenue metrics.
Bayesian Hierarchical Models: Borrowing Strength Across Segments
How hierarchical Bayesian models share information across segments to improve estimates. Learn partial pooling, when it helps, and how to implement it.
Bayesian Methods for Product Decisions: When and Why to Go Bayesian
A comprehensive guide to Bayesian statistics for product analysts. Learn when Bayesian beats frequentist, how posterior probabilities work, and how to make better decisions.
Bayesian Regression: When Shrinkage Improves Predictions
Learn how Bayesian regression uses priors as regularization to improve predictions. Practical guide to shrinkage, uncertainty quantification, and when it beats OLS.
Bayesian Sample Size: Why It's Different and When It Helps
How to plan sample sizes for Bayesian experiments. Learn why Bayesian sample sizing differs from frequentist, and when it gives you smaller or more flexible experiments.
Bayesian vs. Frequentist: A Practical Comparison for Analysts
Side-by-side comparison of Bayesian and frequentist methods for product analysts. Learn which approach fits your problem, team, and decision context.
Credible Intervals vs. Confidence Intervals: What Changes
Understand the real difference between credible and confidence intervals. Learn what each actually means, when it matters, and how to interpret both correctly.
Practical Bayes: Using PyMC, Stan, and brms for Real Analysis
Hands-on guide to Bayesian tools. Compare PyMC, Stan, and brms with real examples. Learn which tool fits your workflow and how to get started fast.