Introduction

Variance and standard deviation are two fundamental concepts in statistics used to measure the spread or dispersion of a dataset. They help us understand how much the values in a dataset deviate from the mean (average).

Variance

Variance quantifies the average squared deviation of each number from the mean of the dataset. It gives a sense of how spread out the data points are.

Formula:
For a population:
$\mathrm{Variance} = \sigma^2 = \frac{1}{N} \sum_{i=1}^{N} (x_i - \mu)^2$

For a sample:
$\mathrm{Variance} = s^2 = \frac{1}{n-1} \sum_{i=1}^{n} (x_i - \bar{x})^2$

Example:
For the dataset [2, 4, 6, 8], the mean is 5.
Variance = [(2-5)² + (4-5)² + (6-5)² + (8-5)²] / 4 = [9 + 1 + 1 + 9] / 4 = 20 / 4 = 5

Standard Deviation

Standard deviation is the square root of the variance. It is expressed in the same units as the original data, making it more interpretable.

Formula:
$\mathrm{Standard\ Deviation} = \sqrt{\mathrm{Variance}}$

Example:
For the dataset above, standard deviation = $\sqrt{5} \approx 2.24$

Comparison: Variance vs. Standard Deviation

• Variance is useful for theoretical work and in formulas, but its squared units can make interpretation tricky.
• Standard deviation is more commonly used in practice because it is in the same units as the data, making it easier to understand the typical distance from the mean.

Conclusion

Variance and standard deviation both measure how spread out a dataset is, but standard deviation is generally more interpretable because it is in the same units as the data. Understanding both helps you better describe and analyze the variability in your data.