StatsTest Blog
Experimental design, data analysis, and statistical tooling for modern teams. No hype, just the math.
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.
How to Write a Methods Section for Internal Docs That's Actually Copy-Ready
Templates and examples for writing clear, reproducible methods sections. Document your analysis so future you (and your colleagues) can understand and replicate it.
Linear Discriminant Analysis
Linear Discriminant Analysis is a statistical test used to predict a single categorical variable using one or more other continuous variables.
Mixed Effects Logistic Regression
Mixed effects logistic regression is a statistical method used to predict a binary variable with one or more other variables with repeated measures.
Mixed Effects Model
A mixed effects model is used for determining the effects of one or more independent variables on a dependent variable when there are repeated measures from the same unit of observation.
Multinomial Logistic Regression
Multinomial Logistic Regression is a statistical test used to predict a single categorical variable using one or more other variables.
Multiple Linear Regression
Multiple Linear Regression is a method used for predicting one continuous variable using one or more other variable or for understanding the numerical relationship between then.
