Statistical Elements of Small Sample Sizes and Only 3 Lots in Process or Product Validation

Attendees of this webinar will learn statistically valid methods for using small sample sizes and for using lots as few as 3 when validating processes or products.

The focus of this webinar is on providing the information needed for attendees to understand the concepts of risk in relation to process validation and to be able to compute sample sizes and lot sizes according the desired or required specification criteria or the process or product.

Why You Should Attend:

Regulatory and certification bodies have requirements for the validation of processes. 21 CFR Part 820.75 (a) Process validation states,” Where the results of a process cannot be fully verified by subsequent inspection and test, the process shall be validated with a high degree of assurance and approved according to established procedures.” Additionally, ISO 9001:2008 7.5.2 Validation of processes for production and service provision states “The organization shall validate any processes for production and service provision where the resulting output cannot be verified by subsequent monitoring or measurement and, as a consequence, deficiencies become apparent only after the product is in use or the service has been delivered.”

Attend this webinar to understand the concepts of risk about process validation and learn to compute sample sizes and lot sizes according to the desired or required specification criteria or the process or product. 100% testing in most manufacturing processes would require 100% destruction of the product being made. Therefore it is important to know the minimum amount of product to sample from a minimum number of lots in order to preserve inventory while minimizing risk (such as loss of product, consumer risk of defectives, etc.)

Learning Objectives:

  • Knowledge of the regulatory requirements of process validation
  • Terminology and concepts of process validation
  • How to calculate the sample size
  • How to calculate number of lots needed.

Areas Covered in the Session :

  • Regulatory Requirements
  • Concepts and Terminology
  • Calculation of sample size to be taken from each lot in a validation study
  • Calculation of confidence and reliability measurement for production process
  • Calculation of confidence and reliability measurement for each lot
  • A worked example will be presented.
  • Example of a summary “justification statement”

Who Should Attend:

  • QA/QC Supervisors
  • Process Engineers
  • Manufacturing Engineers
  • QA/QC Technicians
  • Manufacturing Technicians
  • R&D Engineers


Elaine Eisenbeisz

Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine has over 30 years of experience in creating data and information solutions for industries ranging from governmental agencies and corporations, to start-up companies and individual researchers.

Elaine’s love of numbers began in elementary school where she placed in regional and statewide mathematics competitions. She attended University of California, Riverside, as a National Science Foundation scholar, where she earned a B.S. in Statistics with a minor in Quantitative Management, Accounting. Elaine received her Master’s Certification in Applied Statistcs from Texas A&M, and is currently finishing her graduate studies at Rochester Institute of Technology. Elaine is a member in good standing with the American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.

Elaine has designed the methodology for numerous studies in the clinical, biotech, and health care fields. She currently is an investigator on approximately 10 proton therapy clinical trials for Proton Collaborative Group, based in Illinois. She also designs and analyzes studies as a contract statistician for nutriceutical and fitness studies with QPS, a CRO based in Delaware. Elaine has also worked as a contract statistician with numerous private researchers and biotech start-ups as well as with larger companies such as Allergan and Rio Tinto Minerals. Not only is Elaine well versed in statistical methodology and analysis, she works well with project teams. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals. Please visit the Omega Statistics website at to learn more about Elaine and Omega Statistics.

  Go PRIME   🛈 $ 240 SELECT
  Personal Plan 🛈 $ 3000 SELECT
  Business Plan 🛈 $ 9000 SELECT

  • Login Information with Password to join the session, 24 hours prior to the webinar
  • Presentation Handout in .pdf format
  • Presentation from the Speaker
  • Feedback form
  • Certificate of Attendance
  • Recording access Information with Password to view the webinar, will be sent 24 hours after the completion of the Live webinar.
  • Presentation Handout in .pdf format
  • Certificate of Attendance