Sample Size Determination for Design Validation Activities
Steven Wachs Overview:
Design Validation should ensure that product performance, quality, and reliability requirements are met. In order to have high confidence that products will perform as intended, enough data must be collected and analyzed using various statistical methods. Selecting appropriate sample sizes often vexes many practitioners. Testing only a few units does not provide a high level of confidence that performance requirements will be consistently met. Testing too many units may be unnecessarily expensive and can lead to misleading conclusions.
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 Reliability Demonstration/Estimation, Estimating proportions, Acceptance Sampling for Lot Disposition, and Hypothesis Testing. Numerous examples are provided to illustrate the key concepts and applications.
Why you should Attend:
Sample sizes have a significant impact on the uncertainty in estimates of key process performance characteristics. To have high confidence in results, sufficient sample sizes must be used. Potential problems should be uncovered during Design Validation, prior to launching a product. Failure to do so may result in customer dissatisfaction, excessive warranty, costly recalls, or litigation.
Participants in the webinar will be able to understand the impact of sample sizes on the results from various statistical analysis methods commonly used during Design Validation.
Areas Covered in the Session:
- Populations, Samples, Data Types, and Basic Statistics
- Common Elements of Sample Size Determination
- Design Validation Applications
- Sample Sizes for Reliability Demonstration (Pass/Fail Outcomes)
- Sample Sizes for Reliability Estimation
- Sample Sizes for Estimating Proportion Failing (Pass/Fail Test Outcomes)
- Sample Sizes for Acceptance Sampling / Lot Disposition
- Other Common Sample Size Applications (Hypothesis Testing, Equivalence Testing)
Who Will Benefit:
- Quality Personnel
- Product Design/Development personnel
- Manufacturing Personnel
- Operations / Production Managers
- Production Supervisors
- Supplier Quality personnel
- Quality Engineering
- Quality Assurance Managers, Engineers
- Process or Manufacturing Engineers or Managers
Speaker Profile
Steve Wachs has 30 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.
Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve consults and provides workshops in industrial statistical methods worldwide. He also supports Integral Concepts’ Litigation / Expert Witness practice with data analysis.
Steve possesses an M.A. in Applied Statistics from University of Michigan (Ann Arbor), an M.B.A. from the Katz Graduate School of Business, University of Pittsburgh, and a B.S. in Mechanical Engineering from the University of Michigan (Ann Arbor).