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Sample Size Determination for Design Validation Activities - Webinar (Recorded)

  • Webinar

  • 90 Minutes
  • December 2018
  • Compliance Online
  • ID: 4899753
Why Should You Attend:

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. 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.

This webinar discusses many issues present in any sample size determination. It will highlight 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.

Areas Covered in the Webinar:

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)


Steven Wachs has 25 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.