What Sample Size Do I Need?
Duration: 75 Minutes
The webinar will provide important considerations when selecting sample sizes for specific applications. The knowledge gained by attending the webinar will allow practitioners to consider the implications of sample size selection prior to conducting the study and ensure that the information obtained can be useful for decision making.
Objectives of the Presentation
Why Should you Attend
- Understand the importance of sample size determination
- Learn how to calculate sample sizes for various applications
- Communicate rationale (justification) of selected sample sizes
We often sample from a process or population in order to make an inference about the process based on the sample results. Selecting appropriate sample sizes often vexes many practitioners. This webinar discusses many issues present in any sample size determination. The webinar also discusses several common applications that require an appropriate sample size determination including estimation of product/process performance characteristics, hypothesis tests, acceptance sampling, Statistical Process Control charts, tolerance intervals, and reliability demonstration. When selecting sample sizes, it is important to align the statistical properties of the estimate or test with practical considerations. More data is not always better. Numerous examples are provided to illustrate the key concepts and applications.
Who can Benefit
- Population and Samples
- Basic Statistics
- Common Applications requiring sample size determination (e.g. estimation, hypothesis testing, demonstration of conformance to specification)
- Sample Size Determination (Examples)
The target audience includes personnel involved in product/process development, manufacturing, quality, program management, and business operations. In short anybody using sample data to make decisions.
- R&D Personnel
- Product Development Personnel
- Quality Personnel
- Lab Testing Personnel
- Operations/Production Managers
- Quality Assurance Managers, Engineers
- Process or Manufacturing Engineers or Managers
- Program or Product Managers
- Business Analysts