Sample Size (Basic)

Basic sample size calculator for clinical research planning with common statistical scenarios

Basic sample size estimation for clinical research planning.

Use Cases

• Quick sample size estimates for grant proposals
• Preliminary study design calculations
• Educational purposes for statistics training

Parameters

• test_type: Type of test (t_test, chi_square, proportion)
• alpha: Significance level (default 0.05)
• power: Statistical power (default 0.80)
• effect_size: Expected effect size
• baseline_rate: Baseline proportion (for proportion tests)

Returns

• Required sample size per group
• Total sample size
• Statistical assumptions summary

Example

Input: Two-sample t-test, alpha=0.05, power=0.80, effect_size=0.5 Output: n=64 per group, total=128 subjects

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