Sample Size & Power Calculator (Advanced)

Advanced sample size and power calculations for complex study designs including survival analysis, clustered designs, and multiple comparisons.

Usage

python scripts/main.py --test ttest --effect 0.5 --alpha 0.05 --power 0.8
python scripts/main.py --test survival --hazard-ratio 0.7 --alpha 0.05

Test Types

• t-test (paired/independent)
• Chi-square test
• Log-rank test (survival)
• ANOVA
• Regression
• Clustered designs
• Non-inferiority trials

Parameters

Parameter	Type	Required	Description
--test	string	Yes	Statistical test type (ttest, chi2, survival, anova, regression)
--effect	float	Yes	Effect size (Cohen's d, hazard ratio, etc.)
--alpha	float	No	Significance level (default: 0.05)
--power	float	No	Desired power (default: 0.8)
--allocation	string	No	Group allocation ratio (default: 1:1)

Output

• Required sample size
• Power curve data
• Sensitivity analysis
• Dropout-adjusted N

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python scripts with tools	High
Network Access	External API calls	High
File System Access	Read/write data	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Data handled securely	Medium

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• API requests use HTTPS only
• Input validated against allowed patterns
• API timeout and retry mechanisms implemented
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no internal paths exposed)
• Dependencies audited
• No exposure of internal service architecture

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