Is my Data Abnormal? Normality Tests and Transformations

Why Should You 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).

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.

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 Webinar:

• 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 Will Benefit:

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

Elaine Eisenbeisz
Owner, Omega Statistics

Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California.

Elaine earned her B.S. in Statistics at UC Riverside and received her Master’s Certification in Applied Statistics from Texas A&M.

Elaine is a member in good standing with the American Statistical Association and a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.

Elaine has designed the methodology and analyzes data for numerous studies in the clinical, biotech, and health care fields. Elaine has also works as a contract statistician with private researchers and biotech start-ups as well as with larger companies such as Allergan, Nutrisystem and Rio Tinto Minerals. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals.

Follow us :