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  • A rare, atypical observation.
  • A data value that is numerically distant from the rest of the data.
  • The presence of an outlier may indicate some sort of problem -- possibly a data error or a data value which is fundamentally different from the other data.

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Tip: Identifying outliers...

There is no universally-agreed-on criterion for defining an outlier, however using [math]1.5\, [/math] times the interquartile range (IQR) to identify data values substantially below the first quartile (Q1) or above the third quartile (Q3) provides a good rule of thumb. With this rule an observation is considered a suspected outlier if it is:

  • below [math]\ Q1 - 1.5(IQR)\, [/math]
  • above [math]\ Q3 + 1.5(IQR)\, [/math]

Regardless of your method for identifying outliers, always assess the effects of outliers on the statistical conclusions.


The histogram shown below displays a dataset with one high outlier - a value between 75 and 80, with the next highest value between 50 and 55.


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