Sample Size (Basic)#

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 IndicatorAssessmentLevelCode ExecutionPython/R scripts executed locallyMediumNetwork AccessNo external API callsLowFile System AccessRead input files, write output filesMediumInstruction TamperingStandard prompt guidelinesLowData ExposureOutput files saved to workspaceLow

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#

bash

# 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