The square of standard deviation is called:
(A) Sample Variation
(B) Standard Variance
(C) Sample error
(D) Estimation error
Correct Ans: (B)
Explanation:
The square of the standard deviation is called Standard Variance. Variance measures how much the data points deviate from the mean. By squaring the standard deviation, you get variance, which quantifies the spread of the data more clearly.
The standard deviation represents the average distance between each data point and the mean of the dataset. When you square the deviation, you get the variance, which serves as a useful measure of dispersion in statistics.
Variance is important because it shows how individual data points vary from the mean. However, variance is less intuitive to interpret directly because it uses squared units, unlike standard deviation, which uses the same units as the data.
Therefore, Standard Variance is simply the square of the standard deviation. Statisticians often use variance to understand the variability in a dataset and make inferences about data consistency.
