Final answer:
The median is the best measure of central tendency for interval/ratio data that is skewed, as it is not as influenced by outliers as the mean and more accurately represents the data's center than the mode.
Step-by-step explanation:
The best measure of central tendency for interval/ratio data that is skewed is the median. This is because the median is not as affected by outliers or extreme values as the mean is. In skewed distributions, the mean is pulled towards the long tail, which may not accurately represent the center of the data. The mode indicates the most frequently occurring value, which is not necessarily the center of skewed data. In essence, the median gives a better representation of the central tendency in a skewed distribution since it divides the dataset into two equal halves, with an equal number of observations above and below the median value.
When analyzing symmetrical distributions, the mean, median, and mode will be the same or very close to each other. However, this equality does not hold for skewed distributions. Outliers can significantly affect the mean, making it an unreliable measure of central tendency for skewed data. Hence, when the shape of the data is skewed, selecting the median over the mean or the mode is more appropriate for identifying the middle of the data.