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You shouldn't use the mean in a data set when there's an outlier in the data set. If an outlier is present it will drag the mean either up or down, making the average seem higher or lower than it really is, therefore making it unreliable. HOWEVER:
If you WANT to make the average of the data appear higher or lower (like if you're trying to convince your parents/boss to give you a raise in allowance/salary, and you show them the average income of your peers compared to your average income, you would want your income to appear much less compared to the others), then you should use the mean with as many outliers as possible in order to drag the average up or down.

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Q: When should you not use the mean in a data set?
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It is misleading to use the mean as a descriptor of a data set when the median or mode would be more representative of the data set as a whole.


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Some formulas statisticians may use are population mean, sample mean, variance, and standard deviation. They may also use linear regression line and standard error equations. Another can be the mean value of a data set, where one adds all data points given in a set and divides this number by the number of data points in the set.


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You use the TRIMMEAN function. It calculates the mean taken by leaving out a percentage of data points from the top and bottom of your set of data. You can use this function when you wish to exclude outlying data from your analysis.


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You would use the median if the data were very skewed, with extreme values.


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