Articles exploring experiment-design through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
23 articles
Minimum detectable effect (MDE) is the most important input to A/B test design. Learn how to calculate and choose the right MDE for business impact and traffic.
A practitioner's guide to writing A/B test hypotheses — the structure that survives review, the three failure modes that produce inconclusive tests, and how…
A practical guide for new testing teams, CRO managers, and analysts. The five most common statistical mistakes in DTC A/B testing, why each one happens, and…
Most analysts calculate their experiment baseline from the wrong denominator and the wrong time window.
Standard A/B tests break when users influence each other. Learn how network effects create interference and the experimental designs that handle it.
Master the 'design an A/B test' interview question with a structured framework. Learn the step-by-step approach that impresses hiring managers every time.
Pre-registration locks in your experiment plan before seeing results. Learn why it prevents p-hacking, metric shopping, and post-hoc rationalization.
When A/B tests track multiple metrics, statistical complexity increases. Learn frameworks for managing metric conflicts and making sound decisions.
Guardrail metrics prevent A/B tests from causing hidden damage. Learn how to set them up, monitor them, and use them to make better ship decisions.
Your primary metric determines whether an A/B test succeeds or fails. Learn how to select metrics that are sensitive, aligned, and actionable.
Learn how to design rigorous A/B tests from hypothesis to execution. Covers experiment structure, variable isolation, and common design mistakes.
Underpowered tests waste traffic, miss real wins, and erode trust in experimentation. Learn how to diagnose the problem and fix it before it kills your program.
Statistical power determines whether your A/B test can detect real effects. Most experiments run underpowered, wasting traffic and producing misleading results.
Running A/B tests without proper sample size calculation wastes traffic and produces unreliable results. Learn the inputs, formulas, and practical trade-offs.
MDE isn't a calculator input — it's the foundation of your entire experiment design.
Explore how AI and large language models are transforming A/B test hypothesis generation by eliminating confirmation bias, surfacing non-obvious patterns in…
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…
Master A/B test sample size calculation including the relationship between baseline conversion rate, minimum detectable effect, and statistical power to…
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.
A strong hypothesis is the difference between an experiment that teaches you something and one that wastes traffic.
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.
Anchoring bias silently distorts A/B test results by making the control variant the psychological reference point against which all alternatives are judged…
How to write experiment briefs that prevent last-minute stakeholder rewrites by building alignment into the document structure.