Final answer:
The median is preferred for measuring central tendency when data contains outliers or extreme values because it is not influenced by them, providing a more accurate representation of the central tendency for skewed distributions.
Step-by-step explanation:
The median is the best reason for using the measure of central tendency when dealing with data that contains outliers or extreme values. Because the median represents the middle value of an ordered dataset, it is not influenced by outliers in the same way that the mean is. This characteristic of the median makes it particularly useful for understanding the center of a dataset that has a non-normal distribution or when extreme values might skew the mean. Researchers often prefer the median over the mean in these cases because it provides a more accurate representation of the central tendency of the majority of the data.
For example, in a set of house prices in a neighborhood where most homes are around $300,000, but there are a few mansions priced at several million dollars, the mean would be significantly higher than the median due to the influence of these high-priced homes. Therefore, the median would be a more appropriate measure of central tendency to represent the typical house price in that neighborhood.