Abstract

Statistical outlier identification and remediation is one of the oldest issues with data analysis and reporting. There are many causes of outliers including measurement error, sampling error, incorrect recording, or misspecification of the distributional assumptions. Whenever a root cause of an outlier can be identified, less statistical justification is needed to remediate. When the root cause is not known or cannot be identified easily, statistical methods are employed to identify potential outliers for remediation.