Design experiments for reliable causal evidence.
Experimental Design Specialist
Clinical Trials, A/B Testing, DOE
Best for
- ▸A/B testing sample size calculation for 2% conversion rate improvement detection
- ▸Clinical trial randomization strategy for multi-center drug efficacy study
- ▸Industrial DOE factorial design for optimizing manufacturing process parameters
- ▸Power analysis for behavioral intervention studies in education settings
What you'll get
- ▸Detailed experimental protocol with 12-step methodology, power calculations showing 2,640 users needed per group, stratified randomization by user tenure, and pre-specified analysis plan
- ▸Industrial DOE specification with 2^4 factorial design, blocking strategy for batch effects, response surface methodology recommendations, and statistical analysis framework
- ▸Clinical trial design with CONSORT-compliant protocol, adaptive randomization algorithm, interim analysis plan, and ethical consideration framework
Clear research question, target population, expected effect size, and practical constraints like budget, timeline, and randomization unit definition.
Complete experimental design specification including randomization method, sample size calculations, control structures, and validity threat mitigation strategies.
What's inside
“You are an Experimental Design Specialist. You design statistically rigorous, pragmatically feasible experiments that isolate causal effects and produce actionable evidence across clinical, digital product, and industrial domains. - Prioritize internal validity and statistical efficiency over conven...”
Covers
Not designed for ↓
- ×Data analysis of completed experiments (that's post-experiment analysis)
- ×Statistical modeling or machine learning algorithm selection
- ×Survey design for market research (needs survey design specialist)
- ×Observational study design without experimental intervention
SupaScore
86.88▼
Evidence Policy
Standard: no explicit evidence policy.
Research Foundation: 8 sources (3 books, 2 academic, 1 official docs, 1 paper, 1 industry frameworks)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
v5.5 final distill
Pipeline v4: rebuilt with 3 helper skills
Initial release
Works well with
Need more depth?
Specialist skills that go deeper in areas this skill touches.
Common Workflows
Complete A/B Testing Pipeline
Design rigorous experiment, analyze results, and establish causal conclusions with proper statistical inference
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