Overview 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. 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 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 The Target Audience includes Personnel involved in Product/Process Development and Manufacturing 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 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.