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

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

• 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