Is My Data Abnormal? Normality Tests and Transformations

This webinar will show you how to check for normality in your data and apply transformations to non-normal data. Many of the commonly used statistical tests and calculations of chart limits (or other measurements) require that the data be “normally distributed”.

Learn the theory and concepts of determining when a normal distribution is needed, how to transform data that is not normal, and what to do when transformation does not work.

Why You Should Attend:

The FDA requires that company to have “valid statistical techniques” are “suitable for their intended use”. Many statistical tests require that the distribution of the data used is normal. Assuming that the distribution of data is normal, without checking to see if indeed it is normal, will cause errors in test results.

Errors in results will cause bias of interpretation, rejection of lots that should be passed (or vice versa, passing lots that should be rejected), failing processes that are in specification (or vice versa…), and other problems. In essence, performing many statistical tests and other measurements without the basis of a normal distribution is garbage in, garbage out (GIGO).

In this webinar, you will also learn tools and concepts to understand what makes a distribution normal and when a transformation of data is, or is not, necessary.

Learning Objectives:

  • Understand when a test requires normal data
  • Test and visually inspect data for normality.
  • Learn when a transformation of data is needed, and how to transform data.
  • When the “Normality Assumption” can be “relaxed”.
  • Alternative tests and/or adjustments to use for non-normal data.

Areas Covered in the Session :

  • Regulatory Requirements
  • History of the Normal Distribution
  • The Normal Distribution in Mathematical and Visual Form.
  • To Transform or Not to Transform?
  • Tests to assess the degree of non-normality.
  • Evaluating normality visually.
  • When transformations can do more harm than good.
  • Other Options when Data is Not Normally Distributed

Who Should Attend:

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

FDB3136

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 www.OmegaStatistics.com to learn more about Elaine and Omega Statistics.

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  • 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