Statistical Elements of Sample Size Calculations for Non-Clinical Verification and Validation Studies


Date: 17 Jun '21
Time: 10:00am to 1:00pm (GMT-08:00) Pacific Time (US and Canada); Tijuana
CPD/CME/CEU: 95002

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This webinar provides the logic and processes for determining samples sizes for common tests used in verification or validation of processes. The focus of this webinar is on providing the information needed for attendees to know the appropriate measures and formulas to use for the determining sample size and providing justification for the planned sample sizes.

Why Should You Attend:

Verification and validation studies of design-outputs and/or manufacturing processes are required in many manufacturing processes. However, it can be difficult to understand the rational for same sizes used in these contexts. This webinar will be useful to those interested in learning how to make and justify the reasoning behind sample size determination.
Learn the theory, terminology, regulatory requirements, best practices, and of course, the steps for calculating sample sizes for process verification and validation.

Learning Objectives:

  • Understand statistical concepts and terminology related to sample size determination
  • Use of open-source GPower software to perform sample size calculations
  • How to write a justification statement for the rationale used to determine sample size.

Areas Covered in the Webinar:

  • Regulatory Requirements for sample size in verification and validation
  • Population vs. Sample, Statistical Theory and Terminology
  • Confidence Intervals
  • Statistical Process Control Charts
  • Process Capability Indices
  • Confidence/Reliability Calculations
  • Tests of Significance
  • Mean Time Between Failure (MTBF) studies
  • Example of sample size determination
  • Tips for writing “sample size rationale” statements that are statistically sound.

Who Will Benefit:

  • QA/QC Supervisor
  • Process Engineer
  • Manufacturing Engineer
  • QA/QC Technician
  • Manufacturing Technician
  • R&D Engineer





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