rigor-mcp 0.1.0
Source: Pypi.org· August 18, 2026
SynaBot summary
A new Python library, rigor-mcp, offers developers tools for rigorous statistical validation of AI agents. It provides classical hypothesis testing, effect size calculations, and power analysis, aiming to ensure AI outputs are statistically sound and reliable.
Key takeaways
- New library enhances AI agent statistical validation
- Provides tools for hypothesis testing and effect sizes
- Aims to improve reliability of AI-driven decisions
- Supports CLI and server-based multiple comparisons correction
Why it matters
This development is crucial for professionals integrating AI into critical business functions. It enables more confident deployment of AI agents by providing methods to statistically verify their performance and decision-making processes, reducing the risk of unreliable outcomes.
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