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L16 :Limitation of mean deviation - Statistics, Mathematics, Class 11 Video Lecture

FAQs on L16 :Limitation of mean deviation - Statistics, Mathematics, Class 11 Video Lecture

1. What is mean deviation in statistics?
Ans. Mean deviation is a measure of dispersion that calculates the average deviation of data points from the mean. It is determined by finding the absolute difference between each data point and the mean, summing up these differences, and dividing by the total number of data points.
2. What are the limitations of mean deviation as a measure of dispersion?
Ans. Some limitations of mean deviation as a measure of dispersion include: - It does not take into account the direction of deviations, only their absolute values. - It is influenced by extreme values, making it sensitive to outliers. - It does not provide any information about the distribution of data points. - It is not suitable for mathematical calculations due to the presence of absolute values.
3. How does mean deviation differ from standard deviation?
Ans. Mean deviation and standard deviation are both measures of dispersion, but they differ in their calculation methods. Mean deviation calculates the average deviation of data points from the mean, while standard deviation calculates the square root of the average squared deviation from the mean. Standard deviation is more commonly used as it provides a better understanding of the dispersion of data and is suitable for mathematical calculations.
4. Can mean deviation be negative?
Ans. No, mean deviation cannot be negative. It is calculated by taking the absolute difference between each data point and the mean, ensuring that the deviations are always positive. Consequently, when these deviations are averaged, the mean deviation will always be a positive value or zero if all the data points are equal to the mean.
5. Is mean deviation affected by the size of the data set?
Ans. Yes, the mean deviation is influenced by the size of the data set. As the number of data points increases, the mean deviation tends to become larger. This is because a larger data set provides more opportunities for deviations from the mean. However, it is important to note that the effect of data set size on the mean deviation depends on the values of the data points and their distribution.
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