Why is the mean considered sensitive to outliers?

The mean is considered sensitive to outliers primarily because it encompasses all values in the data set, meaning that any extreme values—whether particularly high or low—can significantly influence the overall calculation. When an outlier is present, it can pull the mean toward itself, resulting in a skewed interpretation of the data. This sensitivity can misrepresent the typical value of the data, especially in distributions where outliers exist.

For instance, in a set of income data where the majority earn between $40,000 and $60,000, but one individual earns $1,000,000, the mean income will be significantly higher than the majority of the population's income, leading to potentially misleading conclusions if the context of data distribution is not considered.

The other choices do not capture the primary reason for the sensitivity of the mean to outliers as effectively. While the mean does require consideration of all values (which is more about accuracy), and it can be less accurate than the median in specific contexts, these aspects are not directly tied to the mean's sensitivity to outliers in the same way that the skewed perception of data is.