A hands-on guide to experimental design covering variables, controls, sampling, randomization, power, pre-registration, and clear success criteria.
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...
Welcome — Design experiments so the answers you get are useful. Designing an experiment is more than picking a control and counting measurements. A well-designed experiment turns curiosity into clear, reproducible evidence you can act on — quickly and with confidence. Teams that rush into testing often discover their...
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Practical guide: Designing experiments that produce interpretable, reproducible results. Good experimental design makes the difference between a result that teaches you something and a result that wastes resources. This guide focuses on the decisions that most often determine whether an experiment is interpretable and...
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{ "FormType": "InteractiveForm", "Title": "Experiment Planning Template (Pre-registration)", "IntroductionHtml": " Capture a concise, shareable pre-registered plan. Keep each answer brief but specific — this is the plan you will follow before looking at outcome data. ", "SubmitLabel": "Save Experiment Plan"...
Pre-experiment readiness checklist. Walk this checklist with your team right before data collection. Each item prevents a common failure mode. Clear primary question: Primary hypothesis and outcome are written and understood by everyone collecting data. Outcome defined exactly: Units, measurement timing, instruments...
Quick experiment design checklist (use right before data collection). Primary hypothesis & outcome written: Primary outcome defined with unit and timing. Success criteria clear: Decision rule or threshold recorded. Sample size considered: Planned sample size and justification noted (or plan to compute it)...
Thinking about sample size: a practical guide (what decisions matter). Sample-size calculations often feel like a math exam. In practice, they are a device for making trade-offs explicit. This guide helps you turn uncertainty, cost, and decision impact into a defensible sampling plan. Start with the decision not the...
{ "FormType": "InteractiveForm", "Title": "Reproducibility Risk Assessment — Experiment", "IntroductionHtml": " Quick, scored assessment to identify reproducibility risks in your experimental plan. For each item select how well the experiment meets the criterion. Lower total score indicates lower reproducibility risk...
Case study: two small experiments, two different decisions. Here are short, realistic scenarios showing how design choices change with context. Bench lab — limited reagents, high cost per run. Hunger: Decide whether changing buffer composition produces a meaningful activity improvement worth further development...
An interactive checklist to confirm an experiment is designed to produce interpretable, reproducible results. Walk through hypothesis, endpoints, controls, sampling and power, protocols, pre-registration, data capture and QC, stopping rules, analysis plans, and compliance. Save answers and notes to record readiness and next steps.
A reproducible, guided pre-registration form that captures study aims, hypotheses, outcomes, sample size logic, analysis steps, handling of missing data, stopping rules, and data/code sharing plans — with fields designed to lock in decisions and reduce post-hoc bias.
Interactive checklist to design, document, and pre-register robust experiments. Captures the research question, hypothesis, design choices, randomization and blinding, sample-size and power details, analysis plan, success criteria, reproducibility and data-sharing plans, ethics and preregistration status, and sign-off — stored for repeatable organizational memory.
A practical, section-by-section experiment protocol and preregistration template that teams can complete prior to data collection. Includes clear prompts for objectives, outcomes, methods, randomization, sample-size justification, data capture formats, QA checks, analysis plans, versioning, and sign-offs—plus a short pre-registration checklist and tips to improve reproducibility.
Concise, practical guidance and worked examples to choose sample sizes for common laboratory and computational studies. Covers core concepts (effect size, alpha, power, variability), simple heuristics, worked calculations for t-tests, ANOVA, and proportions, and pragmatic tips for pilots, adaptive designs, and reporting.
Interactive template to preregister hypotheses, outcomes, analysis plans, ethics and data-sharing details, and to assess preprint readiness. Save structured responses, link to registries and preprint servers, and produce a clear readiness score.