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Before you begin performing analytics on a dataset, it is important to identify and recognize outlier data patterns and values. Unusual values or patterns in the data can be sources for the following:

• Missing data. See Find Missing Data. 
• Bad data. See Find Bad Data.
• Poorly formatted data
• Mismeasured data 
• Data that skews statistics

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• Let them be. If the data is valid, do not remove it unless you have an explicit reason for doing so. 

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• Convert to more meaningful values. You can use the set transform to change outlier values to values that are valid for purposes of analysis.  

  Info
  NOTE: Please be aware that changing of values may impact the validity of your statistical analysis.

  Example of overwriting values where values in the col_numbers column that are below 25 are set to the average value for the column. Otherwise, use the current value:
  
  

  D trans
  Type step p01Name Columns p01Value col_numbers p02Name Formula p02Value IF((col_numbers < 25), AVERAGE(col_numbers), col_numbers) SearchTerm Edit column with formula