How Do We Design Better Experiments?

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Here are the first 5 questions. Create a conversation to invite someone and discuss each card.

1. <section> <h2>Design experiments that teach — not just prove</h2> <p>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.</p> <h3>What you'll get from this resource</h3> <ul> <li>Actionable planning tools you can complete with your team.</li> <li>Simple checklists that reduce common design mistakes.</li> <li>Practical guidance for power, endpoints, controls, blinding, and pre-registration.</li> <li>Examples that show trade-offs for small labs and product teams.</li> </ul> <h3>How to use these pages</h3> <p>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.</p> <p><strong>Who this helps:</strong> 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.</p> </section>
2. <section> <h2>Welcome — Design experiments so the answers you get are useful</h2> <p>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 results are noisy, underpowered, or ambiguous. This guide helps you avoid common mistakes and get experiments that actually move your work forward.</p> <h3>What you'll get here</h3> <ul> <li>A practical orientation to what matters when designing an experiment.</li> <li>A link to a reusable pre-registration worksheet you can save and share.</li> <li>A short checklist for last-minute sanity checks before you run the first sample.</li> <li>A quick reproducibility risk assessment to spotlight weak points before the experiment begins.</li> </ul> <h3>How to use this resource</h3> <p>Start by reading the practical guide to learn the key choices that influence interpretability and reproducibility. Then open the <strong>Experiment Planning Template</strong> to capture a short pre-registered plan that includes your hypothesis, primary endpoint, planned sample size and analysis method. Use the <strong>Quick Checklist</strong> right before you run the first experiment and the <strong>Reproducibility Risk Assessment</strong> to quantify and discuss weak spots with collaborators.</p> <h3>Quick promise</h3> <p>Complete the planning template and the checklist before data collection begins. That single habit removes ambiguity, reduces post-hoc decisions, and makes your results easier to interpret and reproduce.</p> <p><em>When you find yourself thinking, "We can decide analysis later," pause and capture a brief plan instead. It will save time and reduce rework.</em></p> </section>
3. <section> <h2>Practical guide: Designing experiments that produce interpretable, reproducible results</h2> <p>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 reproducible.</p> <h3>Begin with the question and the measurable outcome</h3> <p>Start by turning your curiosity into a testable hypothesis and a clear primary outcome. A hypothesis is useful only when it points to an observable difference or relationship you can measure. The primary outcome should be one specific measurement (or composite) and include the unit, timing, and how it will be collected.</p> <h3>Define success up front</h3> <p>Write a simple success criterion: what result would lead you to conclude the hypothesis is supported, and what would cause you to reject it. Explicit success criteria reduce ambiguous post-hoc interpretations.</p> <h3>Controls, baseline, and experimental contrast</h3> <p>Choose controls that isolate the effect you want to test. Consider whether you need positive and negative controls, and be explicit about baseline rates or measurements. For many applied tests, the key is a contrast large enough to matter operationally, not just statistically.</p> <h3>Sample size and power</h3> <p>Decide the effect size you care about (the minimum meaningful difference) and estimate the variation you expect. Use those numbers to calculate the sample size needed to detect the effect with adequate power (commonly 80% or 90%). Underpowered studies are a leading cause of ambiguous or irreproducible findings.</p> <h3>Randomization and blocking</h3> <p>Randomize treatment assignment to avoid allocation bias. When known nuisance variables (e.g., batch, operator, site) could influence outcomes, use blocking or stratification so those effects are balanced across groups.</p> <h3>Blinding and objective measurement</h3> <p>Whenever feasible, blind data collectors and analysts to condition assignment. If blinding is impossible, use objective, predefined measurements and document how subjective judgments are handled.</p> <h3>Pre-registration and analysis plans</h3> <p>Record your primary hypothesis, primary outcome, planned sample size, and the primary analysis method before you look at the outcome data. Pre-registration reduces selective reporting and analytical flexibility that inflate false positives.</p> <h3>Data quality and reproducible workflows</h3> <p>Plan how data will be recorded, validated, and stored. Include versioned protocols, equipment calibration records, raw and processed data locations, and where analysis scripts will live. Reproducibility requires that someone can follow your steps months later.</p> <h3>Common mistakes to avoid</h3> <ul> <li>Collecting data first and deciding the primary outcome later.</li> <li>Failing to justify sample size or relying only on convenience samples.</li> <li>Using multiple unplanned analyses and reporting only the significant ones.</li> <li>Omitting essential metadata such as exact protocol steps, reagents, or equipment settings.</li> </ul> <h3>Practical trade-offs</h3> <p>Not every test needs the longest form of pre-registration or the largest sample possible. For early exploration, smaller, rapid experiments can be useful if treated as pilots and explicitly labeled as exploratory. When decisions will depend on the result, invest more effort in design, powering, and documentation.</p> <h3>Next steps</h3> <p>Use the Experiment Planning Template to capture a short pre-registered plan, run the Quick Checklist before collecting data, and use the Reproducibility Risk Assessment to discuss remaining weak points with your team. These steps together turn good intent into disciplined practice.</p> <footer> <p><strong>Glossary (quick):</strong> Primary outcome — the single main measurement you use to judge the hypothesis. Power — the probability the experiment will detect the pre-specified effect size if it exists. Pre-registration — recording your hypothesis and analysis plan before seeing the outcome data.</p> </footer> </section>