# Where would you use mean, median, mode and why?

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Average (mean) is the most widely used measure of central tendency, but is not always the most appropriate.

a. Please provide unique examples of where one would use median, mode, mean, and why.

b. Please provide an unique example of where mean, median, and mode are appropriate measures in one's professional life.

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The expert determines where you would use mean, median and mode and why.

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a. Please provide unique examples of where one would use median, mode, mean, and why.

The thing to remember about the sample mean value is that it is extremely sensitive to the presence of outliers in a data set. Outliers may be defined several different ways, but the bottom line is that an outlier is any value that is "extreme" in relationship to the other values in the dataset; either extremely "large", or extremely "small". "Large outliers in a dataset will cause the sample mean value to be larger than it should be (thereby overestimating the population mean value) and small outliers in a dataset will cause the sample mean value to be smaller than it should be (thereby underestimating the population mean value).

For a given numerical dataset you should remember that it is possible to calculate all three measures of central tendency (the mean, median and the mode), but care should be taken to decide which one to use based on characteristics of the dataset. The choice between using the sample mean and median basically comes down to the presence (or absence) of outliers in the dataset. If outliers are present then the median is a better choice than the sample mean as your measure of central tendency because the median is NOT sensitive to the presence of outliers, whereaa the sample mean is. If there are no outliers to worry ...

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