AS – Sample Size Determination for Design Validation Activities

When:
February 12, 2019 @ 9:00 am – 10:00 am
2019-02-12T09:00:00-08:00
2019-02-12T10:00:00-08:00
Where:
webinar

https://www.audiosolutionz.com/manufacturing/sample-size-determination-design-validation-activities.html

By Steven Wachs

$199

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 collected and analyzed through various statistical methods. You should be able to uncover potential problems during the design validation phase—before launching your product. Failure to do so may result in customer dissatisfaction, excessive warranty, costly recalls, or litigation.
 
Learn the secrets to sample size determination in this webinar by expert speaker Steven Wachs,and uncover potential problems in the design validation stage itself. 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.

As a practitioner, you know how hard it can be to select an appropriate sample size. Testing only a few units does not provide a high level of confidence that performance requirements will be consistently met; and testing too many units may be unnecessarily expensive, and can lead to misleading conclusions. The numerous examples provided in this webinar to illustrate the key concepts and applications will go a long way to clear your doubts on an issue that often vexes many industry practitioners.

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.

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