Final answer:
When a data set contains extreme outliers, the median is preferred over the mean as it provides a more accurate center of the data. correct option is B.
Step-by-step explanation:
Yes, you would be better off calculating the median instead of the mean if there are extreme outliers in the data. When data has outliers or extreme values, they can significantly skew the mean, making it a less reliable measure of central tendency. The median, being the middle value when all numbers are sorted, is not influenced by the magnitude of outliers, thus giving a better representation of the center of the dataset.
Data that is measured on a ratio scale benefits from having a true zero point and allows for ratio comparisons, which is not directly related to the choice between using mean or median. The decision to use mean or median in this case would still depend on the presence of outliers and the symmetry of the distribution. For symmetrical distributions with no extreme outliers, the mean could be an appropriate measure. However, if even in ratio scale data, there is a significant skew or outliers, the median would be more representative.
In a skewed distribution, other measures like quartiles are also informative. They provide a more nuanced understanding of data spread which complement the analysis using the median. It is customary to represent skewed data visually through graphs like histograms or box plots for better comprehension. Remember, when faced with skewed data or outliers, the median typically offers a more accurate sense of the 'center' than the mean.