The variance and standard deviation are both measures of dispersion or spread in a dataset, but they differ in their interpretation and mathematical computation:

1. Variance: It measures how much the data points differ from the mean, on average, but in squared units. The formula for variance ($\sigma^2$ for a population or $s^2$ for a sample) is: $\text{Variance} = \frac{1}{n} \sum_{i=1}^{n} (x_i - \mu)^2$ where $x_i$ represents each data point, $\mu$ is the mean of the data, and $n$ is the number of data points.

2. Standard Deviation: It is the square root of the variance and expresses the spread in the same units as the data itself. The formula for standard deviation ($\sigma$ for a population or $s$ for