Wednesday 12:25
in Helium3
Modern data science demands flexible statistical methods that can handle sequential data analysis and continuous monitoring. Traditional p-values, while widely used, have limitations when dealing with sequential testing scenarios. This talk introduces a Python library that implements e-values and e-processes, offering a more natural approach to measuring statistical evidence and enabling true sequential testing.
Outline:
Introduction to E-Values
- The limitations of p-values in sequential testing
- What are e-values and why do they matter?
- Key advantages: intuitive interpretation, composability, and sequential validity
- Real-world applications: A/B testing, clinical trials, anomaly detection
Library Overview and Core Features
- Architecture and design philosophy
- Key components:
- Sequential testing framework
- Confidence sequences
- Standard (classical) testing with e-values
- Conformal e-testing
- Integration with existing Python data science tools
- Live coding demonstrations of basic usage
Practical Applications
- A/B testing with continuous monitoring
- Sequential hypothesis testing
- Adaptive experimental design
- Real-time anomaly detection
- Live demonstrations with real-world datasets
Advanced Features and Extensions
- Conformal prediction integration
- CUSUM procedures for change detection
- Adaptive thresholding
- Power analysis for e-value tests
- Performance considerations and optimizations
Best Practices and Guidelines
- When to use e-values vs. traditional methods
- Setting up monitoring frameworks
- Common pitfalls and how to avoid them
- Tips for production deployment
Future Directions and Community Involvement
- Roadmap for future development
- How to contribute
- Resources for learning more
The talk will include interactive demonstrations and real-world examples, making complex statistical concepts accessible to practitioners. Attendees will leave with:
- Understanding of e-values and their advantages
- Practical knowledge of implementing sequential testing
- Hands-on experience with the library
- Best practices for production deployment
- Resources for further learning
