Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.
$npx -y skills add k-dense-ai/claude-scientific-skills --skill experimental-designInstalls into the current project.
Run `npx skills use "https://github.com/k-dense-ai/claude-scientific-skills" --skill "k-dense-ai/claude-scientific-skills/experimental-design"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/k-dense-ai/claude-scientific-skills" that are relevant to the current task. Run `npx skills add "https://github.com/k-dense-ai/claude-scientific-skills"` and select the relevant skills, then follow their instructions.