| name | statistical-analysis-advisor |
| description | 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. |
| version | 1.0.0 |
| category | Data |
| tags | [] |
| author | AIPOCH |
| license | MIT |
| status | Draft |
| risk_level | Medium |
| skill_type | Tool/Script |
| owner | AIPOCH |
| reviewer | |
| last_updated | 2026-02-06 |
Statistical Analysis Advisor
Intelligent statistical test recommendation engine that guides users through selecting the right statistical methods for their data.
Capabilities
-
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
-
Assumption Checking
- Normality tests (Shapiro-Wilk, Kolmogorov-Smirnov)
- Homogeneity of variance (Levene's test, Bartlett's test)
- Independence verification
- Outlier detection guidance
-
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
from scripts.main import StatisticalAdvisor
advisor = StatisticalAdvisor()
recommendation = advisor.recommend_test(
data_type="continuous",
groups=2,
independent=True,
distribution="normal"
)
assumptions = advisor.check_assumptions(
data=[group1, group2],
test_type="independent_ttest"
)
power = advisor.calculate_power(
effect_size=0.5,
alpha=0.05,
sample_size=30
)
Input Parameters
| Parameter | Type | Description |
|---|
| data_type | str | "continuous", "categorical", "ordinal" |
| groups | int | Number of groups/comparison levels |
| independent | bool | Independent or paired/related samples |
| distribution | str | "normal", "non-normal", "unknown" |
| sample_size | int | Current or planned sample size |
Technical Difficulty: High ⚠️
Warning: Statistical recommendations have significant implications for research validity. This skill requires human verification of all recommendations before application in published research.
References
- See
references/statistical_tests_guide.md for detailed test selection criteria
- See
references/assumption_tests.md for assumption checking procedures
- See
references/power_analysis_guide.md for power calculation methods
Limitations
- Does not perform actual data analysis (recommendations only)
- Cannot access raw data directly
- Complex multivariate designs may require specialized consultation
- Bayesian alternatives not covered comprehensively
Risk Assessment
| Risk Indicator | Assessment | Level |
|---|
| Code Execution | Python/R scripts executed locally | Medium |
| Network Access | No external API calls | Low |
| File System Access | Read input files, write output files | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Output files saved to workspace | Low |
Security Checklist
Prerequisites
pip install -r requirements.txt
Evaluation Criteria
Success Metrics
Test Cases
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- Performance: Large dataset → Acceptable processing time
Lifecycle Status
- Current Stage: Draft
- Next Review Date: 2026-03-06
- Known Issues: None
- Planned Improvements:
- Performance optimization
- Additional feature support
