StatsTest Blog
Experimental design, data analysis, and statistical tooling for modern teams. No hype, just the math.
Collinearity: When It Breaks Interpretation and What to Do
A practical guide to multicollinearity in regression. Learn when collinearity is a problem, how to detect it, and practical solutions that don't involve blindly dropping variables.
Feature Scaling and Transforms: When Preprocessing Changes the Story
A practical guide to standardization, centering, and transformations in regression. Learn when scaling affects interpretation, when it's required, and how to interpret coefficients on transformed variables.
Interaction Terms: When Treatment Effects Vary by Segment
A practical guide to interaction effects in regression. Learn when to include interactions, how to interpret them correctly, and common pitfalls when testing whether treatment effects differ across segments.
Linear Regression Assumptions and Diagnostics in Practice
A practical guide to checking linear regression assumptions with diagnostic plots. Learn what violations actually look like, when they matter, and what to do when assumptions fail.
Logistic Regression for Conversion: Interpretation and Common Pitfalls
A practical guide to logistic regression for product analysts. Learn to interpret odds ratios correctly, avoid common mistakes, and communicate results to stakeholders who don't think in log-odds.
Poisson vs. Negative Binomial: Modeling Counts and Rates
A practical guide to choosing between Poisson and negative binomial regression for count data. Learn to detect overdispersion, handle excess zeros, and interpret rate ratios correctly.
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.
Regression vs. t-Test vs. ANOVA: The Unifying View (and When the Simpler Tool Suffices)
Understand how t-tests, ANOVA, and regression are all the same underlying model. Learn when to use the simpler approach and when regression's flexibility is worth it.