Need expert advice on statistical analysis methods.
Statistical Analysis Advisor
Expert statistical methodology guidance
Best for
- ▸Power analysis for clinical trial sample size determination
- ▸Multiple comparison correction for genomics studies with thousands of tests
- ▸Causal inference design for marketing attribution using instrumental variables
- ▸Bayesian A/B test interpretation with informative priors
What you'll get
- ▸Detailed power analysis with Cohen's effect size calculations and minimum detectable effect recommendations
- ▸Step-by-step experimental design critique with specific assumption violations flagged and alternative approaches suggested
- ▸Bayesian vs frequentist method comparison with prior sensitivity analysis recommendations for the specific use case
Clear research question, data structure details, sample size constraints, and whether the analysis is exploratory or confirmatory.
Specific statistical method recommendations with assumption checks, power calculations, multiple comparison strategies, and guardrails against statistical malpractice.
What's inside
“You are a Statistical Analysis Advisor. You combine mathematical rigor with pragmatic judgment to guide users toward robust, reproducible analyses while actively preventing p-hacking, HARKing, and other questionable research practices grounded in Wasserstein et al. (2019), Simmons et al. (2011), Gel...”
Covers
Not designed for ↓
- ×Writing statistical analysis code or programming implementation
- ×Basic data cleaning and preprocessing tasks
- ×Creating visualizations or dashboards
- ×Machine learning model selection and hyperparameter tuning
SupaScore
86.63▼
Evidence Policy
Standard: no explicit evidence policy.
Research Foundation: 8 sources (5 books, 2 paper, 1 academic)
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
Auto-versioned: masterfile quality gate passed (score: 86.0)
Initial release
Works well with
Need more depth?
Specialist skills that go deeper in areas this skill touches.
Common Workflows
Rigorous A/B Testing Pipeline
Statistical design validation, followed by test implementation and specialized experimental controls
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