Free Webinar:
Sample Size for Frequentist and Bayesian Statistical Intervals
In this tutorial, Paul Murphy, research statistician at nQuery, will explore sample size determination for statistical intervals, providing practical guidance and real-world examples to illustrate best practices in this field.
Learning objective of this webinar:
We'll delve deep into sample size determination for statistical intervals, offering both theoretical foundations and practical applications that you can implement immediately in your research. We will explore four key areas:
1. An Overview of Common Statistical Intervals
- Fundamental concepts and historical development
- Modern applications and implementation
- Comparative advantages across different methodologies
- Practical considerations for implementation
2. Sample Size for Confidence Intervals
- Traditional approaches and modern methodologies
- Optimization techniques and practical limitations
- Real-world applications and case studies
- Implementation strategies for different study designs
3. Sample Size for Prediction and Tolerance Intervals
- Future observation forecasting techniques
- Population percentile estimation methods
- Reliability considerations in various contexts
- Quality control applications
- Manufacturing specifications and guidelines
4. Sample Size for Bayesian Credible Intervals
- Prior distribution selection strategies
- Posterior probability interpretation
- Hybrid approaches combining frequentist and Bayesian methods
- Computational considerations
- Decision-making applications
About nQuery
nQuery helps make your clinical trials faster, less costly and more successful.
It is an end-to-end platform covering Frequentist, Bayesian, and Adaptive designs with 1000+ sample size procedures.
Sample Size & Power Calculations
Calculate for a Variety of frequentist and Bayesian Design
