StatsTest Blog
Experimental design, data analysis, and statistical tooling for modern teams. No hype, just the math.
Two-Way ANOVA vs. Regression: Understanding Interactions for Product Teams
When to use two-way ANOVA versus regression for analyzing experiments with multiple factors. Covers interactions, main effects, and practical interpretation for product analytics.
Visual Diagnostics for Group Comparisons: The Plots That Matter
How to visually check assumptions for ANOVA and other group comparisons. Covers boxplots, Q-Q plots, residual plots, and interaction plots with interpretation guidance.
Welch's T-Test vs. Student's T-Test: Why You Should Always Use Welch's
A definitive comparison of Welch's and Student's t-tests. Learn why the equal variance assumption fails in practice and why Welch's should be your default.
When Confidence Intervals and P-Values Seem to Disagree
Understand why CIs and p-values sometimes appear to conflict and how to resolve these apparent contradictions. Learn common scenarios and the correct interpretation.
When to Say 'Inconclusive': Decision Rules That Build Trust
Knowing when to call an experiment inconclusive is a skill. Learn decision frameworks for ambiguous results that maintain credibility and enable good business decisions.
Why Revenue Is Hard: Log-Normal Distributions and Heavy Tails
A deep dive into why revenue metrics are statistically challenging. Learn about log-normal distributions, heavy tails, whale effects, and practical approaches to analyzing revenue in A/B tests.
Winsorization and Trimming: When Acceptable and How to Disclose
Practical guide to handling extreme values in product metrics. Learn when Winsorizing or trimming is appropriate, how to choose cutoffs, and how to report results transparently.