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Median: Measures of Central Tendency Video Lecture | Business Mathematics and Statistics - B Com

FAQs on Median: Measures of Central Tendency Video Lecture - Business Mathematics and Statistics - B Com

1. What is the median, and how is it different from the mean and mode?
Ans. The median is the middle value in a data set when the numbers are arranged in ascending or descending order. It differs from the mean, which is the average of all values, and the mode, which is the most frequently occurring value in the data set. The median is particularly useful for skewed distributions as it is not affected by extreme values.
2. How do you calculate the median in a data set with an even number of values?
Ans. To calculate the median in a data set with an even number of values, first arrange the numbers in order. Then, find the two middle numbers and calculate their average. For example, in the data set {3, 5, 7, 9}, the two middle numbers are 5 and 7, so the median is (5 + 7) / 2 = 6.
3. Can the median be used for both numerical and categorical data?
Ans. The median is primarily used for numerical data, as it requires an ordered arrangement of values to identify the middle point. It is not applicable to categorical data, which can be better represented using mode or frequency distributions.
4. What are the advantages of using the median as a measure of central tendency?
Ans. The advantages of using the median include its robustness against outliers, making it a reliable measure in skewed distributions. It provides a better representation of the typical value in such cases compared to the mean, which can be significantly impacted by extreme values.
5. In what real-life scenarios is the median a more effective measure than the mean?
Ans. The median is more effective in scenarios such as income levels, home prices, or test scores where data may have extreme outliers. For instance, in a community where a few individuals earn significantly higher incomes than the rest, the median income will reflect the typical income level more accurately than the mean.
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