Why is the median often preferred in skewed distributions?

The median is often preferred in skewed distributions because it is not affected by outliers. In a skewed distribution, the presence of extreme values can significantly distort measures of central tendency such as the mean, making it an unreliable indicator of the "center" of the data. The median, however, is calculated based on the middle value in a dataset when arranged in order, meaning that extreme values do not influence its position. This makes the median a more robust measure, providing a clearer representation of the central tendency in situations where data is not symmetrically distributed.

In contrast, the mean may be pulled in the direction of the skew, while the median remains more stable, providing a better summary of the typical value in the dataset. This characteristic allows researchers and analysts to better understand the central location of data in conditions where variations are pronounced and can lead to misleading interpretations otherwise.