StatsTest Blog
Experimental design, data analysis, and statistical tooling for modern teams. No hype, just the math.
A/B Testing Statistical Methods for Product Teams: The Complete Guide
A comprehensive guide to statistical methods for A/B testing in product development. Learn when to use z-tests, chi-square, sequential testing, CUPED, and how to handle the real-world messiness of experimentation.
Analytics Reporting That Doesn't Get You Killed in Review
How to communicate statistical results to stakeholders without getting destroyed in review. Templates, common mistakes, and strategies for building trust through transparency.
Assumption Checks and What To Do When They Fail
A comprehensive guide to statistical assumptions in hypothesis testing. Learn which assumptions matter most, how to diagnose violations, and what to do when your data doesn't fit the textbook requirements.
Comparing More Than Two Groups: A Complete Guide
How to compare means, medians, and distributions across three or more groups. Covers ANOVA, Kruskal-Wallis, post-hoc tests, and when each method is appropriate.
Effect Sizes, Confidence Intervals, and Practical Significance
A comprehensive guide to quantifying and communicating the magnitude of effects. Covers standardized and raw effect sizes, confidence intervals, and when statistical significance doesn't mean practical importance.
Metric Distributions in Product Analytics: Heavy Tails, Skew, and What to Do
A comprehensive guide to handling real-world metric distributions in product analytics. Learn why revenue is hard, how to deal with zeros, when to transform vs. use robust methods, and how to communicate results on skewed data.
Model Evaluation & Human Ratings Significance for AI Products
Statistical rigor for ML/AI evaluation: comparing model performance, analyzing human ratings, detecting drift, and making defensible decisions. A comprehensive guide for AI practitioners and product teams.
Picking the Right Test to Compare Two Groups: A Decision Framework
A comprehensive guide to choosing between t-tests, Mann-Whitney, bootstrap, and other methods when comparing two groups. Covers continuous, binary, and count data with practical decision trees.
Regression for Analysts: From Comparison to Causal Insight
A comprehensive guide to regression analysis for product and data analysts. Learn when to use linear, logistic, and count regression, how to diagnose problems, interpret coefficients correctly, and avoid common pitfalls that lead to misleading conclusions.
Time-to-Event and Retention Analysis: Survival Methods for Tech
A comprehensive guide to survival analysis for product analysts. Learn Kaplan-Meier curves for retention, log-rank tests for comparing groups, Cox regression for understanding drivers, and how to handle the unique challenges of tech product data.