Normality Tests and Normality Transformations: Explanations, Justifications, and Uses

John N Zorich

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The calculations used in many statistical tests and methods require that the inputted data be “normally distributed”. This webinar explains what it means to be “normally distributed”, how to assess normality, how to test for normality, and how to transform non-normal data into normal data, and how to justify the transformations to internal and external quality system auditors.

Why Should You Attend:

Being able to assess whether data is “normally distributed”, and to be able to "transform to normality" is critical to ensuring that a company's “valid statistical techniques” are “suitable for their intended use” (as required by the FDA). Therefore, it is critical to a company's success. Most users of statistics make the error of assuming normality, in order to simplify their statistical analyses. However, most data sets in industry are not normally distributed, and not noticing that oftentimes results in rejecting lots that should have passed, failing processes that actually met their validation criteria, or keeping products in R&D long after they should have been transferred to Manufacturing.

The calculations used in many statistical tests and methods require that the inputted data be “normally distributed”. Such calculations include those for Student's t-Tests, ANOVA tables, F-tests, Normal Tolerance limits, and Process Capability Indices. Unless the raw data used in such calculations is “normally distributed”, the resulting conclusions may be incorrect.

Dimensional data (length, width, height) are typically normally distributed. But many other types of data sets are almost always non-normal, such as: tensile strength, burst pressure, and time or cycles to failure. Some non-normal data can be transformed into normality, in order to then allow statistical calculations to be valid when run on the transformed data.

This webinar explains what it means to be “normally distributed”, how to assess normality, how to test for normality, and how to transform non-normal data into normal data, and how to justify the transformations to internal and external quality system auditors.

Areas Covered in the Webinar:

  • Regulatory requirements
  • Binomial distribution
  • Historical origin of the Normal distribution
  • Normal distribution formula, histogram, and curve
  • Validity of Normality transformations
  • Necessity for transformation to Normality
  • How to use Normality transformations
  • Normal Probability Plot
  • How to evaluate Normality of raw data and transformed data
  • Significance tests for Normality
  • Evaluating the results of a Normality test
  • Recommendations for implementation
  • Recommended reference textbooks

Who Will Benefit:

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

From Medical Device, Pharmaceutical, and any Industry that performs standard statistical analyses.

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Physical CD-DVD of recorded session will be despatched after 72 hrs on completion of payment


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Recorded video session



Speaker: John N Zorich , Statistical Consultant and Trainer, Statistical Consult

John 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.


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