Final answer:
The median is the best measure of central tendency when addressing skewed data as it is less affected by outliers and extreme values compared to the mean, providing a more accurate representation of the central data point.
Step-by-step explanation:
When data is skewed, the measure of central tendency that is generally considered to be the best representative of the central data is the median. Unlike the mean, which can be heavily influenced by outliers and extreme values, the median serves as a robust measure that is not affected as much by skewness. The mode denotes the most frequently occurring value in a dataset and may not represent the centre accurately, especially in a skewed distribution.
If the distribution of the data is skewed to the left, the hierarchy is typically mean < median < mode. Conversely, if the data is skewed to the right, the relationship is mode < median < mean. In cases of skewness, using the median as the central tendency measure provides a better indication of a dataset's central value than the mean, which is pulled toward the tail. Therefore, for skewed distributions, the median is the most appropriate measure to consider for the centre of the data.