Articles exploring new-analysts through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
6 articles
Most stakeholder-submitted hypotheses describe a goal ("make X clearer") instead of an intervention. Why that breaks execution today and meta-analysis tomorrow — and a falsifiability standard with templates, bad-to-better rewrites, and the vague-word vocabulary every CRO team eventually has to address.
A bare point estimate is uninterpretable. This guide walks through the 5 elements that should appear on every test readout — power analysis, MDE, confidence interval, SRM check, and segment cuts — with plain-English definitions, quick-reference formulas, and a copy-paste template you can adopt today.
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 exactly what to do instead — with quick rules of thumb, definitions, and Monday-morning action items.
New CRO analysts learn the field through case studies — but case studies are curated by construction, and reading them as evidence produces an inflated model of what good A/B testing looks like. This guide teaches you how to extract the useful parts without being misled by the curation, with a 5-step reading protocol you can apply today.
A clear, plain-English guide to the most under-taught concept in DTC experimentation. Learn what regression to the mean is, why it makes your big A/B test wins disappoint on rollout, and the shrinkage table you can use to set realistic expectations on any reported lift.
Early stopping is the single most common methodological mistake in new testing programs. This guide explains exactly what peeking does to your false positive rate, why the 10-day test pattern recurs everywhere, and how to fix it with a pre-registered stopping rule. Includes Monday-morning checklist.