Final answer:
When describing the typical value in a data set with outliers, the median should be used. Outliers are significantly different from other data points and can skew the mean, whereas the median, being the middle value, is unaffected by extreme values.
Step-by-step explanation:
If the goal is to describe the "typical" value in a data set that contains an outlier, the median should be calculated rather than the mean. The median provides a better estimate of the center for skewed data or data with outliers. An outlier is a value that is significantly different from other values in the data set and can affect the mean disproportionately.
While the mean takes into account all values, making it sensitive to outliers, the median represents the middle value when the data points are arranged in order, which is not affected by extreme values. The mode, which is the most frequently occurring value, can also provide insight into the dataset but isn't necessarily a measure of centrality if the data is not bimodal or multimodal.
To detect outliers, one can observe the data graphically, such as on a scatter plot, where outliers can be identified as points that lie more than two standard deviations (represented as 2s) away from the line of best fit. In regression analysis, any residual more than two standard deviations from the predicted value can be considered an outlier.