Identifying Outliers In Process Data Using Visual And Analytical Techniques

Identifying outlier data points using visual and analytical techniques is especially important for proper process validation, control, and monitoring in the FDA regulated industries.

A confirmed outlier data point should never be deleted; however, it should be excluded from any subsequent calculations. Best practice is to provide the rationale and the method used to determine if the suspect point(s) is/are indeed outliers and document the cause(s) for the outlier(s).

We need to identify and consider excluding outlier data points:

  • to provide a realistic picture of a process
  • to provide meaningful control limits
  • to prevent “bonus” statistical control limits
  • to ensure actions are taken only when appropriate,

What are your thoughts on outliers and how they affect data?

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