Design experiments that teach — not just prove

Most experiments fail to deliver useful learning because they mix unclear goals, weak measurements, and wishful analysis. This resource helps teams design experiments that produce interpretable, reproducible conclusions you can act on — even when time, samples, or budget are limited.

What you'll get from this resource

• Actionable planning tools you can complete with your team.
• Simple checklists that reduce common design mistakes.
• Practical guidance for power, endpoints, controls, blinding, and pre-registration.
• Examples that show trade-offs for small labs and product teams.

How to use these pages

Start by clarifying a single, testable primary question. Use the interactive experiment planner to capture decision points (hypothesis, primary outcome, analysis plan). Complete the pre-experiment checklist before you run the first sample. If your result matters beyond the team, save a pre-registration record to reduce bias later. When in doubt about sample size or analysis choices, consult a statistician — but use the sample-size guide here to ask the right questions first.

Who this helps: bench scientists, engineers running pilots, product teams running A/B tests, clinical researchers planning feasibility studies, and small labs that must make every experiment count.