Make Growth Decisions You Can Defend

Data Segmentation for A/B Tests: Finding Hidden Winners

Discover how to uncover segment-level insights hidden within overall A/B test results.

client-side testingMar 27, 20267 min read

Client-Side vs Server-Side A/B Testing: Choosing the Right Architecture

Understand the tradeoffs between client-side and server-side A/B testing architectures.

What to Do With Inconclusive A/B Test Results

Inconclusive A/B test results are not failures. Learn how to extract learning from flat tests, distinguish between wrong hypotheses and weak…

croMar 27, 20267 min read

How to Archive A/B Tests: Building Organizational Knowledge

Transform your A/B testing program from isolated experiments into a compounding knowledge system.

croMar 27, 20268 min read

How to Prioritize A/B Tests: Frameworks That Actually Work

Move beyond gut-feel prioritization with structured frameworks for ranking A/B test hypotheses.

External Validity Threats: Why Your A/B Test Results Might Not Generalize

Discover the external validity threats that can invalidate your A/B test results, from seasonality and sample pollution to the flicker effect, and how to…

Statistical Power in A/B Testing: Avoiding False Negatives

Learn what statistical power means for A/B testing, why 80% is the standard, and how underpowered tests lead to costly false negatives that cause you to…

Sample Size Calculation for A/B Tests: Getting It Right Before You Start

Master A/B test sample size calculation including the relationship between baseline conversion rate, minimum detectable effect, and statistical power to…

P-Values Demystified: What They Actually Mean for Your A/B Tests

Understand what p-values really mean in A/B testing, why common interpretations are wrong, and how to use statistical significance correctly for business decisions.

ab-testingMar 27, 20266 min read

One-Tailed vs Two-Tailed Tests: Does It Actually Matter?

Understand the difference between one-tailed and two-tailed hypothesis tests in A/B testing, when each is appropriate, and the simple conversion rule between them.

Bayesian vs Frequentist A/B Testing: What Practitioners Need to Know

A practical guide to the Bayesian vs Frequentist debate in A/B testing, why it matters less than you think, and what practitioners should actually focus on…

ab-testingMar 27, 20269 min read

How Long Should You Run an A/B Test? The Complete Duration Guide

Learn the science behind A/B test duration, why stopping at significance is dangerous, and how to determine the right test length using sample size…

ab-testingMar 27, 20266 min read

Confidence Intervals and Margin of Error: Reading A/B Test Results Correctly

Learn how to interpret confidence intervals and margin of error in A/B test results, why your conversion rate is always an estimate with uncertainty, and…

conversion insightsMar 27, 20268 min read

User Testing for Conversion: Watching Real Users Navigate Your Product

User testing reveals the gap between how you designed your product and how people actually experience it.

croMar 27, 20268 min read

How to Write A/B Test Hypotheses That Actually Generate Insights

A strong hypothesis is the difference between an experiment that teaches you something and one that wastes traffic.

click mapsMar 27, 20268 min read

Mouse Tracking and Heat Maps: What They Actually Tell You (And What They Don't)

Heat maps and session replays are seductive but easy to misinterpret. Learn how to use click maps, scroll maps, and form analytics to generate real insights…

conversion killersMar 27, 20267 min read

Technical Analysis: The Low-Hanging Fruit Most Optimizers Miss

Technical bugs and performance issues silently destroy conversion rates.

conversion researchMar 27, 20267 min read

Conversion Research: The Foundation of Every Winning A/B Test

Most A/B tests fail because they skip the research phase. Learn how conversion research — from heuristic analysis to qualitative methods — builds the…

behavioral insightsMar 27, 20268 min read

Qualitative Research for CRO: Why Numbers Alone Aren't Enough

Quantitative data tells you what is happening on your website. Qualitative research tells you why.

conversion auditMar 27, 20268 min read

Heuristic Analysis for Conversion Optimization: A Systematic Framework

Learn the five-dimension heuristic evaluation framework — relevancy, clarity, value, friction, and distraction — and how to score pages systematically for…

ab-testingMar 27, 20264 min read

Regression to the Mean: Why Your A/B Test Winner Might Disappear

Regression to the mean explains why early A/B test results often look dramatic but fade over time.

Multivariate Testing vs A/B Testing: Which Should You Use?

Understand how multivariate testing works, when it outperforms A/B testing, the traffic requirements for MVT, and why most programs run roughly ten A/B…

A/B/n Testing Explained: When and Why to Test Multiple Variants

Learn what A/B/n testing is, how traffic splits work with three or more variants, when you need multiple variants, and the tradeoffs compared to simple A/B tests.

ab-testingMar 27, 20269 min read

False Positives in A/B Testing: The Silent Killer of Growth Programs

Why false positives are the biggest threat to A/B testing programs, how A/A tests prove the problem is real, and why stopping at significance is the number…