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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.