Design of Experiments & A/B Toolkit

Practical recipes and guidance to design, power, run, analyze, and scale causal experiments and A/B tests for teams and organizations.

Toolkit

Design of Experiments & A/B Testing Toolkit

A practical, team-ready toolkit to design, power, run, analyze, and interpret A/B and causal experiments. Includes clear sample-size formulas and worked examples, a copy‑ready pre-registration template, analysis and interpretation checklists, guardrails for multiple testing, and a short primer on common threats to validity — plus practical next steps to make the toolkit interactive and repeatable across your organization.

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Research Project

Experimentation & Learning Lab Kit

A reproducible, actionable lab kit to design, run, analyze, document, and scale experiments. Includes an experiment charter and risk checklist, causal-design and power guidance, an experiment registry schema, a standard analysis notebook (with effect-size summaries and decision thresholds), governance checklists, and a rollout/productionization decision framework to turn short tests into lasting organizational learning.

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Template

Experiment & A/B Test Template (Design of Experiments)

A practical, reproducible experiment template that guides teams from clear hypothesis through pre-registered analysis, instrumentation, stopping rules, guardrails, rollout criteria and production handoff. Includes a filled example (illustrative) and a link to a power calculator.

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Playbook

Experiment Runbook: From Hypothesis to Learning

A practical, step-by-step playbook that helps teams prepare, run, analyze, and archive experiments so results are reliable, interpretable, and reusable. Includes actionable templates, checklists, decision rules, guardrails, and a troubleshooting guide for common analysis errors.

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Template

Design of Experiments One‑Pager (A/B & Causal Tests)

An interactive, saveable experiment-design template that captures hypothesis, primary/secondary metrics, power/sample notes, randomization, guardrails, analysis plan, decision rules, and reproducibility artifacts. Includes a brief worked example and internal readiness checklist.

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