Practical guidance, templates, and a reproducible organizational blueprint to design, run, analyze, and scale experiments and pilots.
Interactive checklist and setup form to stand up a small experimentation lab, covering mission, roles, tooling, evidence standards, governance, review cadence, onboarding, adoption paths, and a simple triage rubric to prioritize experiments.
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.
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.
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.
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.
An interactive preregistration form to capture hypothesis, metrics, population, instrumentation, analysis plan, risks, guardrails, and decision rules so experiments produce reproducible, actionable learning.
A practical, reusable template and operational checklist to move validated experiments into production while measuring how impact changes at scale. Includes decision gates, a complete measurement plan template (primary/secondary/guardrail metrics), a post-rollout monitoring checklist, a reproducibility & handover checklist, and a simple ROI/impact-to-cost prioritization rubric.
A complete, practical template and guidance for recording experiments, results, decisions, and reusable artifacts so teams can learn faster, avoid repeated failures, and surface trustworthy evidence across projects and sites.
A practical dashboard template that makes live experiments, results, and lessons visible across the organization. Includes recommended widgets, visualizations, decision-rule templates, governance notes, data requirements, and guidelines for linking each card back to the experiment registry so teams can reuse findings and avoid duplication.
An interactive, standard experiment card teams can fill to add experiments, outcomes, and reusable evidence to the Experiments Repository / Evidence Bank. Includes structured metadata, evidence-quality checklist, decision linkage, tags for discovery, and guidance for retention and reuse.
An interactive, saveable canvas to capture experiment intent, metric definitions, scaling risks, data and operational requirements, monitoring and rollback plans, and a concrete readiness checklist to decide when to move experiments into production.
A practical, team-friendly guide to framing valid causal tests, sizing and sampling experiments, avoiding common biases, running clean analyses, and turning results into decisions. Includes compact hypothesis templates, a short power heuristic, a pre-launch checklist, an analysis checklist, and clear decision rules for adopt/iterate/stop.
A concise, decision-oriented catalog of common analysis recipes (cohort, anomaly, causal, regression, funnel, time series, segmentation, diagnostic) with when-to-use guidance, required data, quick implementation checklists, validation steps, interpretation tips, and common pitfalls.
Practical decision rules, measurement patterns, and an operational handover checklist to move validated experiments into production while tracking how impact changes as you scale.
A practical, non-technical primer that helps teams tell causal stories from noisy evidence: identify when correlations mislead, decide when to run experiments, learn simple observational adjustments, design small randomized tests, avoid common pitfalls, and capture results so your organization actually learns.