In the second scenario, after the lottery winnings, the mean is $1,002, and the median is $3.

In the first scenario, before the lottery winnings, the mean is $3, and the median is also $3.

You'll notice that the median is $3 in both scenarios, but the mean goes from...

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In the second scenario, after the lottery winnings, the mean is $1,002, and the median is $3.

In the first scenario, before the lottery winnings, the mean is $3, and the median is also $3.

You'll notice that the median is $3 in both scenarios, but the mean goes from $3 all the way up to $1,002.

In situations where there is an outlier, it is better to use the median as a way to represent the data points that you have. In the second scenario, there is one outlier. That outlier is Kid #5, who brought $5,000 to the store. If you want an accurate representation of the majority of the data (that is, Kids #1–4), using the median is a good way to show that the vast majority of the kids (80%) have only a few dollars.

Here is a scenario in everyday life where the median is a better representation of data. Think of a nice, small town with one hundred houses. Let's say 99 of those houses are small, cute little houses worth around $250,000. In the corner of this same town, one very successful millionaire owns a giant mansion worth $3,000,000. In this scenario, think of the millionaire as Kid #5 from earlier. It would be better to report the median property value of $250,000, because it represents the vast majority of homeowners in the area. It gives the reader of this data a better idea of what the town is like. The outlying millionaire would skew the data if you chose to represent the home values using the mean rather than the median.