Outliers (DP IB Applications & Interpretation (AI)): Revision Note

Outliers

What are outliers?

• Outliers are extreme data values that do not fit with the rest of the data

  • They are either a lot bigger or a lot smaller than the rest of the data

• Outliers are defined as values that are more than 1.5 × IQR from the nearest quartile

  • x is an outlier if x < Q₁ - 1.5 × IQR or x > Q₃ + 1.5 × IQR

• Outliers can have a big effect on some statistical measures

Should I remove outliers?

• The decision to remove outliers will depend on the context

• Outliers should be removed if they are found to be errors

  • The data may have been recorded incorrectly

  • For example: The number 17 may have been recorded as 71 by mistake

• Outliers should not be removed if they are a valid part of the sample

  • The data may need to be checked to verify that it is not an error

  • For example: The annual salaries of employees of a business might appear to have an outlier but this could be the director’s salary