Practical guidance, templates, and a reproducible organizational blueprint to design, run, analyze, and scale experiments and pilots.
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.
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.