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Introduction to Mean Deviation Video Lecture | SSC CGL Tier 2 - Study Material, Online Tests, Previous Year
FAQs on Introduction to Mean Deviation Video Lecture - SSC CGL Tier 2 - Study Material, Online Tests, Previous Year
| 1. What is mean deviation and how is it calculated? |  |
Ans. Mean deviation is a statistical measure that quantifies the average distance between each data point and the mean of the dataset. To calculate it, follow these steps: 1) Find the mean of the dataset, 2) Subtract the mean from each data point to find the absolute deviations, 3) Average those absolute deviations by summing them up and dividing by the number of data points.
| 2. How does mean deviation differ from standard deviation? | |
Ans. Mean deviation and standard deviation both measure the spread of data points around the mean, but they do so differently. Mean deviation uses absolute values, making it simpler to understand, while standard deviation squares the deviations, which can emphasize larger discrepancies. As a result, standard deviation is more sensitive to outliers than mean deviation.
| 3. In what situations is mean deviation preferred over standard deviation? | |
Ans. Mean deviation is preferred in situations where a simpler interpretation of data spread is needed, particularly when dealing with non-normal distributions or when the dataset includes outliers. It is also useful in certain fields, like finance, where understanding average deviations from the mean is more crucial than emphasizing larger variances.
| 4. Can mean deviation be negative? | |
Ans. No, mean deviation cannot be negative. Since it measures the absolute differences between data points and the mean, all deviations are non-negative values. When these absolute values are averaged, the result will also be a non-negative number.
| 5. What are the limitations of using mean deviation? | |
Ans. The limitations of mean deviation include its inability to effectively handle outliers and its lack of sensitivity compared to standard deviation. Additionally, mean deviation may not be as widely recognized or used in statistical analysis as standard deviation, which can limit its application in more advanced statistical methods.
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