statistical-analysis-advisor
Recommends appropriate statistical methods (T-test vs ANOVA, etc.) based on dataset characteristics, performs assumption checking, and provides power analysis guidance. Trigger when user asks about choosing statistical tests, checking statistical assumptions, or needs guidance on experimental design and sample size calculations.
npx skills add majiayu000/claude-skill-registry --skill statistical-analysis-advisor --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Statistical Analysis Advisor Intelligent statistical test recommendation engine that guides users through selecting the right statistical methods for their data. ## Capabilities 1. **Statistical Test Selection** - Compares and recommends between T-test, ANOVA, Chi-square, Mann-Whitney, Kruskal-Wallis, etc. - Considers data type, distribution, sample size, and research question - Provides decision tree logic for test selection 2. **Assumption Checking** - Normality tests (Shapiro-Wilk, Kolmogorov-Smirnov) - Homogeneity of variance (Levene's test, Bartlett's test) - Independence verification - Outlier detection guidance 3. **Power Analysis & Sample Size** - Effect size estimation (Cohen's d, eta-squared, Cramér's V) - Sample size calculations for desired power - Post-hoc power analysis ## Usage ```python from scripts.main import StatisticalAdvisor advisor = StatisticalAdvisor() # Get test recommendation recommendation = advisor.recommend_test( data_type="continuous", groups=2, independent=True, distribution="normal" ) # Check assumptions assumptions = advisor.check_assumptions( data=[group1, group2], test_type="independent_ttest" ) # Power analysis power = advisor.calculate_power(
- Capabilities
- Usage
- Input Parameters
- Technical Difficulty: High ⚠️
- References
- Limitations
- Risk Assessment
- Security Checklist
- Prerequisites
- Evaluation Criteria
- Success Metrics
- Test Cases
- Lifecycle Status
Python dependencies pip install -r requirements.txt
What does the statistical-analysis-advisor skill do?
Recommends appropriate statistical methods (T-test vs ANOVA, etc.) based on dataset characteristics, performs assumption checking, and provides power analysis guidance. Trigger when user asks about choosing statistical tests, checking statistical assumptions, or needs guidance on experimental design and sample size calculations.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill statistical-analysis-advisor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.