Is the mean sensitive to outliers?

The mean is indeed sensitive to outliers, which is why the correct answer is affirmative. Outliers are values that significantly differ from the rest of the data set, and they can skew the mean, leading it to represent the data inaccurately. For instance, if the majority of data points in a set are clustered around a certain value, but one or two points are extremely high or low, the mean will shift closer to those outliers, which may not reflect the true central tendency of the data.

In contrast, other measures of central tendency, such as the median, are not influenced by outliers in the same way, as the median considers the position of values rather than their magnitude. Therefore, when assessing a data set, reliance solely on the mean can lead to misinterpretation, especially in fields where outliers might be prevalent.

While it's true that the degree of sensitivity may vary based on the data distribution or the number of data points, the fundamental nature of the mean as a measure makes it susceptible to extreme values across various contexts, reinforcing that it is indeed sensitive to outliers. This characteristic is important to consider in evidence-informed practice to ensure accurate data analysis and interpretation.