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

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(
    effect_size=0.5,
    alpha=0.05,
    sample_size=30
)

Input Parameters

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

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. 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