Valid Statistical Rationales for Sample Sizes
This webinar provides guidance on how to justify such sample sizes, and thereby indirectly provides guidance on how to choose sample sizes.
More Trainings by this Expert
This webinar explains the logic behind sample-size choice for several statistical methods that are commonly used in verification or validation efforts, and how to express a valid statistical justification for a chosen sample size.
The statistical methods discussed during the webinar include the following:
Why should you Attend:
- Confidence intervals
- Process Control Charts
- Process Capability Indices
- Confidence / Reliability Calculations
- MTBF Studies ("Mean Time Between Failures" of electronic equipment)
- QC Sampling Plans
Almost all manufacturing and development companies perform at least some verification testings or validation studies of design-outputs and/or manufacturing processes, but it is sometimes difficult to explain the rationale for the sample sizes used in such efforts.
This webinar provides guidance on how to justify such sample sizes, and thereby indirectly provides guidance on how to choose sample sizes. Those justifications can then be documented in Protocols or regulatory submissions, or can be given to regulatory auditors who may ask for them during onsite audits at your company. Thus, this webinar is designed to help you avoid regulatory delays in product approvals and to prevent an auditor from issuing you a nonconformity.
NOTE: This webinar does not address rationales for sample sizes used in clinical trials.
Areas Covered in the Session:
Who Will Benefit:
- Examples of regulatory requirements related to sample size rationale
- Sample versus Population
- Statistic versus Parameter
- Rationales for sample size choices when using
- Confidence Intervals
- Attribute data
- Variables data
- Statistical Process Control C harts (e.g., XbarR)
- Process Capability Indices (e.g., Cpk )
- Confidence/Reliability Calculation
- Attribute data
- Variables data (e.g., K-tables)
- Significance Tests ( using t-Tests as an example )
- When the "significance" is the desired outcome
- When "non-significance" is the desired outcome (i.e., "Power" analysis)
- AQL sampling plans
- Examples of statistically valid "Sample-Size Rationale" statements
- QA/QC Supervisor
- Process Engineer
- Manufacturing Engineer
- QA/QC Technician
- Manufacturing Technician
- R&D Engineer
John N. Zorich has spent almost 40 years in the medical device manufacturing industry; the first 20 years were as a "regular" employee in the areas of R&D, Manufacturing, QA/QC, and Regulatory; the next 15 years were as a consultant in the areas of QA/QC and Statistics. These last few years were as a trainer and consultant in the area of Applied Statistics only. His consulting clients in the area of statistics have included numerous start-ups as well as large corporations such as Boston Scientific, Novellus, and Siemens Medical.
His experience as an instructor in applied statistics includes having given annual 3-day seminars for many years at Ohlone College (San Jose CA), and previously having given that same course for several years for Silicon Valley ASQ Biomedical. He's given numerous statistical seminars at ASQ meetings and conferences. And he creates and sells validated statistical software programs that have been purchased by more than 110 companies, world-wide.