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Limitation of Mean, Median, Mode - Statistics Video Lecture - Class 11
FAQs on Limitation of Mean, Median, Mode - Statistics Video Lecture - Class 11
| 1. What is the limitation of using mean as a measure of central tendency? |  |
Ans. One limitation of using the mean as a measure of central tendency is that it is sensitive to outliers. If there are extreme values in the data set, the mean can be significantly influenced and may not accurately represent the typical value of the data.
| 2. What is the limitation of using median as a measure of central tendency? | |
Ans. One limitation of using the median as a measure of central tendency is that it does not take into account the actual values of the data. It only considers the order or rank of the values. Therefore, the median may not provide a complete picture of the data distribution or the individual values themselves.
| 3. What is the limitation of using mode as a measure of central tendency? | |
Ans. One limitation of using the mode as a measure of central tendency is that it may not exist or may be unstable in certain data sets. If there are multiple values that occur with the same highest frequency, there can be no unique mode. Additionally, if the data is continuous, the mode may not accurately represent the central value.
| 4. How does the sensitivity to extreme values affect the mean? | |
Ans. The sensitivity to extreme values means that even a single outlier can significantly affect the mean. The mean is calculated by summing up all the values and dividing it by the total number of values. If there is an outlier with a very large or small value, it can pull the mean towards that extreme, resulting in a distorted representation of the typical value of the data.
| 5. In what situations is the median a more appropriate measure of central tendency than the mean? | |
Ans. The median is a more appropriate measure of central tendency than the mean in situations where the data set contains outliers or extreme values. Since the median is not influenced by these outliers, it provides a better representation of the typical value of the data. Additionally, the median is often preferred when dealing with skewed distributions, as it is not affected by the skewness.
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