Design Validation should ensure that product performance, quality, and reliability requirements are met. In order to have high confidence that product 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.
Steven Wachs, will walk you through the common elements of sample size determination, along with specific sample size applications for various design validation activities—such as Reliability Demonstration/Estimation, Acceptance Sampling for Lot Disposition, Estimating Proportions, and Hypothesis Testing.
Session Highlights:
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)
Why You Should Attend:
After attending this session, you will understand the impact of sample sizes on the results from statistical analysis methods commonly used during design validation. You will be confident in your ability to uncover potential problems during the design validation stage of product development, thereby ensuring that your product performance, quality, and reliability requirements are optimally met, and that your products perform as intended.
Who Should Attend:
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
*You may ask your Question directly to our expert during the Q&A session.
** You can buy On-Demand and view it as per your convenience.
Steven Wachs
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 to estimate and reduce warranty. In addition to providing consulting services, Steve regularly conducts workshops in industrial statistical methods for companies worldwide.