Normality Testing: Applications and Issues

Live Webinar

  • 90 minutes
  • 31 Days Left
     Feb 24, 2021
  •   01:00 PM - 02:30 PM ET
    10:00 AM - 11:30 AM PT

Many types of statistical analyses assume that the underlying raw data follow a Normal Distribution.  Common examples include Analysis of Variance (ANOVA), t-tests, F tests, and Process Capability analyses using Normal methods.  It is important to test the assumption of normality before using methods that require it. 

Next, several methods for testing data for normality are presented.  Although some older techniques are referenced, we emphasize the use of probability plotting and goodness-of-fit tests to provide objective assessments of normality.  We also discuss the risks of making errors in hypothesis tests and how to control those risks. 

We provide several common scenarios that lead to the rejection of normality.  An understanding of these situations is important for determining appropriate actions when a normality test fails. We discuss outliers, unstable processes, and issues caused by discreteness in the data. Next, we discuss some of the common types of goodness-of-fit tests that may be used (e.g. Andersen-Darling, Kolmogorov Smirnoff, etc.). 

Join this session by expert speaker Steven Wachs, where he will discuss probability distributions and the Normal (Gaussian) Distribution specifically.  The key characteristics and distribution parameters that define the normal model are discussed in the introduction.  The concept of distribution model fitting is presented and reasons for normality testing are reviewed. 

Session Highlights: 

  • Understand the Normal Distribution and how it is characterized 

  • Know when normality testing is important to 

  • Apply probability plotting and goodness-of-fit tests for testing normality of the data 

  • Interpret graphical results and p-values from normality testing 

  • Diagnose why normality tests fail to 

  • Understand the differences between some of the common goodness-of-fit tests 

  • Determine appropriate sample sizes for normality testing 

  • Perform and interpret outlier tests 

  • Understand the justification for excluding data from normality tests 

Why You Should Attend: 

By attending this webinar you will be able to understand the following: 

  • In-Depth Treatment of Normality Testing 

  • Focus on Effective Application of the Techniques 

  • Improve your Ability to Conduct, Interpret, and Explain Test Results

  •  Normality Testing Methods 

  • Reasons for Rejecting Normality 

Who Should Attend? 

  • Data Analysts 

  • Quality Engineering or Quality Assurance Personnel 

  • Product Design and Development personnel 

  • Manufacturing personnel 

  • Supplier Quality personnel 

  • Process Engineers 

  • Six Sigma Green Belts or Black Belts 

  • Scientists 

  • R&D Personnel 

*You may ask your Question directly to our expert during the Q&A session. 

** You can buy On-Demand and view it at your convenience.

Steven Wachs

Steven Wachs

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as to estimate and reduce warranty. In addition to providing consulting services, Steve regularly conducts workshops in industrial statistical methods for companies worldwide.

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