StatsTest Blog
Experimental design, data analysis, and statistical tooling for modern teams. No hype, just the math.
Cox Proportional Hazards: What 'Proportional' Actually Means
A practical guide to Cox regression for product analysts. Learn what the proportional hazards assumption means, how to check it, what to do when it fails, and how to interpret hazard ratios correctly.
CUPED and Variance Reduction: When It Helps and When It Backfires
Learn how CUPED (Controlled-experiment Using Pre-Experiment Data) can dramatically reduce variance in A/B tests, when to use it, and the pitfalls that can make it backfire.
Dealing with Zeros: Zero-Inflated and Two-Part Models
How to handle metrics with many zeros—revenue from non-purchasers, engagement from inactive users, events that didn't happen. Learn when to use zero-inflated models, two-part models, and simpler alternatives.
Delta Method vs. Bootstrap: When Each Is Appropriate
A practical guide to choosing between delta method and bootstrap for variance estimation. Learn when each approach excels, their assumptions, and how to implement both.
Drift Detection: KS Test, PSI, and Interpreting Signals
How to detect when your model's inputs or outputs have shifted. Learn about KS tests, Population Stability Index, and when drift actually matters.
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.
Effect Sizes for Mean Differences: Cohen's d, Hedges' g, and Raw Differences
A practical guide to effect sizes for comparing means. Learn when to use standardized vs. raw effect sizes, how to calculate and interpret them, and how to report them properly.
Effect Sizes for Proportions: Risk Difference, Risk Ratio, and Odds Ratio
A practical guide to effect sizes when comparing rates and proportions. Learn when to use risk difference vs. risk ratio vs. odds ratio, and how to interpret each correctly.
Equal Variance and Welch's T-Test: When It Actually Matters
A deep dive into the equal variance assumption for t-tests and ANOVA. Learn when violations are problematic, how to detect them, and why Welch's correction should be your default.
Experiment Guardrails: Stopping Rules, Ramp Criteria, and Managing Risk
Protect your experiments and users with proper guardrails. Learn when to stop an experiment, how to safely ramp exposure, and what metrics should trigger automatic rollback.
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.