Sample Size Determination for Design Validation Activities
This webinar discusses many issues present in any sample size determination, 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.
October 19, 2020
10:00 AM PDT | 01:00 PM EDT
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Product Id : 503363
Live: One Dial-in One Attendee
Corporate Live: Any number of participants
Recorded: Access recorded version, only for one participant unlimited viewing for 6 months ( Access information will be emailed 24 hours after the completion of live webinar)
Corporate Recorded: Access recorded version, Any number of participants unlimited viewing for 6 months ( Access information will be emailed 24 hours after the completion of live webinar)
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.
Statistical Methods are typically used to ensure that product performance, quality, and reliability requirements are met during the Design Validation phase of product development.
This webinar discusses common elements of sample size determination and several specific sample size applications for various design validation activities including Reliability Demonstration/Estimation, Acceptance Sampling, and Hypothesis Testing. Numerous examples are provided to illustrate the key concepts and applications.
Why should you 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:
Who Will Benefit:
- 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)
- 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
Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. He 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.
Mr. Wachs 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. Mr. Wachs regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.
He has an M.A. in Applied Statistics from the University of Michigan, an M.B.A, Katz Graduate School of Business from the University of Pittsburgh, 1992, and a B.S., Mechanical Engineering from the University of Michigan.