What is the Difference Between Variance and Standard Deviation?

Variance and standard deviation are both measures of dispersion in a dataset, indicating how spread out the data is. However, they differ in their calculation and interpretation:

• Variance: It is the average of the squared deviations from the mean. In other words, it measures how much the numbers in a dataset vary from the mean, with each difference being squared to ensure that the result is always positive. Variance is expressed in squared units, making it less intuitive to understand compared to standard deviation.
• Standard Deviation: It is the square root of the variance. Standard deviation measures how far apart the numbers are in a dataset. It is expressed in the same units as the original data, making it easier to interpret and understand. Standard deviation is often used to compare the dispersion of data across different datasets.

In summary, both variance and standard deviation are used to measure the spread of data in a dataset, but they differ in their calculation, interpretation, and units. Variance is the average of squared deviations from the mean, while standard deviation is the square root of the variance.

Comparative Table: Variance vs Standard Deviation

Here is a table summarizing the key differences between variance and standard deviation:

In summary, variance and standard deviation are both measures of dispersion in a data set, but they differ in their calculation, units, and the information they provide. Variance describes the average squared deviations from the mean, while standard deviation measures the average deviation from the mean in terms of absolute value and is represented in the same units as the data.