A/B Testing Guides & Best Practices
Practical articles on experimentation, statistics, and building a reliable testing culture.
How to Write a Good A/B Test Hypothesis
Vague ideas produce vague results. A clear hypothesis names the change, the user, the metric, and the expected direction — before you ship code.
A/B vs A/B/C Testing — When Multi-Variant Is Worth It
Extra variants cost sample size and invite multiple comparisons. Use A/B/C when learning requires it — not because the design tool makes it easy.
Primary vs Guardrail Metrics — How to Choose What You Optimize
One metric to decide. A few to protect the business. Everything else is diagnostic — and mixing those roles creates false ships.
What Is SRM in A/B Testing — And What to Do When It Fires
Sample Ratio Mismatch means your traffic split isn't what you intended. Ignore it and your "winning" variant may be a data bug.
Bayesian vs Frequentist A/B Testing — A Practical Comparison
p-values and posteriors answer different questions. Here's when each framework helps — and when it doesn't — for product experiments.
Confidence Intervals in A/B Testing — Why They Beat a Naked p-Value
A p-value only asks “different from zero?” A confidence interval answers “how big might the lift be?” — which is what product decisions actually need.
Multiple Comparisons in A/B Tests — Without Overcorrecting
Testing 20 metrics will produce false greens. Bonferroni and FDR help — but for product decisions, metric families and the overall evidence picture matter more than academic purity.
Why Log-Transform Revenue Metrics in A/B Tests
Money metrics are right-skewed. Log-transforming them makes tests more reliable — and the results still map cleanly to percent lifts.
How Long Should You Run an A/B Test? Avoiding the Peeking Problem
Stopping early is the most common A/B testing mistake. Learn how to calculate the right duration before your experiment starts.
What is a p-value? A Plain-English Guide for A/B Testers
P-values are widely misunderstood — even by experienced analysts. Learn what a p-value actually means and how to use it correctly.
A/B Test Sample Size: How Many Users Do You Actually Need?
Running an experiment before you have enough data is one of the most common — and costly — mistakes in A/B testing.