Respuesta :
The median is usually preferred to other measures of central tendency when your data set is skewed (i.e., forms a skewed distribution) or you are dealing with ordinal data. However, the mode can also be appropriate in these situations, but is not as commonly used as the median.
INTERPRETATION-
If the data being analyzed is qualitative, then the only measure of central tendency that can be reported is the mode. However, if the data is quantitative in nature (ordinal or interval/ratio) then the mode, median, or mean can be used to describe the data.
With quantitative data, the shape of the distribution of scores (symmetrical, negatively or positively skewed) plays an important role in determining the appropriateness of the specific measure of central tendency to accurately describe the data. If the distribution of scores is symmetrical or nearly so, the median and mean (as well as the mode) will be real close to each other in value.
In this case, the mean is the value of central tendency that is usually reported. However, if the distribution of scores is positively or negatively skewed, the mean will tend to either overestimate (in positively skewed distributions) or underestimate (in negatively skewed distributions) the true central tendency of the distribution. In extreme cases of skewed data, the mean can lie at a considerable distance from most of the scores.
Therefore, in skewed distributions, the median will tend to be the more accurate measure to represent the data than the mean because the median can never have more than one half the scores above or below it.
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