Why sample variance divided by n-1

Population Variance \(\sigma^2 = \frac{1}{N} \sum_{i=1}^{N} (X_i - \mu)^2\)

Sample Variance \(s^2 = \frac{1}{n-1} \sum_{i=1}^{n} (x_i - \bar{x})^2\)

When dealing with statistics, the calculation of sample variance and standard deviation plays a crucial role in estimating population parameters. One key aspect that often raises questions is the use of \(n-1\) instead of \(n\) in the denominator when working with a sample. Let’s explore the reasons behind this choice.

Degrees of Freedom

In statistical terms, degrees of freedom represent the number of independent pieces of information or values in a calculation. When calculating the sample variance, we use the sample mean (\(\bar{X}\)) as an estimate of the population mean (\(\mu\)). This estimation process introduces a constraint, as the last observation is not free to vary, given the requirement that the sample mean must be satisfied. As a result, we have \(n-1\) degrees of freedom in the context of the sample data.

Unbiased Estimation

The use of \(n-1\) in the denominator is associated with Bessel’s correction, a method to ensure that the sample variance is an unbiased estimator of the population variance. Without this correction, the sample variance tends to underestimate the population variance, especially for small sample sizes. The correction compensates for the slight bias introduced when using the sample mean in the variance calculation.

Consistency with Larger Samples

While the difference between using \(n\) and \(n-1\) becomes negligible as the sample size (\(n\)) increases, this correction is more critical for smaller sample sizes. It aims to provide a more accurate and unbiased representation of the population variance, aligning the estimation with the true characteristics of the population.

In summary, the choice of \(n-1\) in the sample variance and standard deviation formulas is a statistical adjustment that acknowledges the loss of degrees of freedom when estimating population parameters from a sample. This adjustment enhances the accuracy and unbiased nature of the estimation, particularly in scenarios involving smaller sample sizes.