Webinar Duration: 90 minutes

RECORDED: Access recorded version only for one participant; unlimited viewing for 6 months (Access information will be emailed 24 hours after the completion of payment)

SPEAKER: John N. Zorich

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:

Confidence intervals
Process Control Charts
Process Capability Indices
Confidence / Reliability Calculations
MTBF Studies (“Mean Time Between Failures” of electronic equipment)
QC Sampling Plans

Why should you Attend: 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:
– Introduction
– 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

Who Will Benefit:
– QA/QC Supervisor
– Process Engineer
– Manufacturing Engineer
– QA/QC Technician
– Manufacturing Technician
– R&D Engineer

John N. Zorich has spent 35 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 last 15 years were as consultant in the areas of QA/QC and Statistics. 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 statistics includes having given 3-day workshop/seminars for the past several years at Ohlone College (San Jose CA), 1-day training workshops in SPC for Silicon Valley Polytechnic Institute (San Jose CA) for several years, several 3-day courses for ASQ Biomedical, numerous seminars at ASQ meetings and conferences, and half-day seminars for numerous private clients. He creates and sells formally-validated statistical application spreadsheets that have been purchased by more than 75 companies, world-wide.