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Sample Size (Basic)

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

personAuthor: aipoch-aihubclawhub
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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 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