# How to Calculate Sample Variance (SV)

Sample variance, often denoted as ‘s^2,’ is a measure used to determine how spread out a data set is. It helps reveal the degree to which the individual data points differ from the mean value of the entire sample. This article will guide you through the step-by-step process of calculating sample variance.

**Steps to Calculate Sample Variance (SV)**

**1. Gather your data set:** The first step to calculate sample variance is to collect all the individual data points in your sample group. Make sure you are using a good representation of the population you want to study.

**2. Calculate the mean (average) value:** To find the mean value (also known as an average), add up all the data points in your sample and then divide by the number of observations (n). The formula for this is:

Mean = (Sum of all data points) / n

**3. Subtract each data point from the mean and square the result:** Next, subtract each data point from the calculated mean and square each of these differences. Write down these squared differences for every data point, as they are an important part of calculating sample variance.

**4. Calculate the sum of squared differences:** Add up all these squared differences obtained in step 3. This will be used in the next step.

**5. Divide by (n-1):** Divide the result obtained in step 4 by one less than the total number of observations or (n-1). This step helps provide a more accurate measure of variability in your sample because it accounts for potential bias, especially when estimating small samples.

Formula to calculate Sample Variance:

s^2 = [(Σ(x_i – Mean)^2)] /(n-1)

Where:

s^2 = sample variance

Σ = sum (sigma notation)

x_i = each individual observation

Mean = average value among all observations

n = number of observations

**Sample Variance Calculation Example**

Let’s take an example data set with five observations: 2, 4, 6, 8, and 10.

1. Mean = (2+4+6+8+10) / 5 = 30/5 = 6

2. Subtract each data point from the mean and square the result:

(2-6)^2 =16

(4-6)^2 =4

(6-6)^2 =0

(8-6)^2 =4

(10-6)^2 =16

3. Sum of squared differences = (16+4+0+4+16) = 40

4. Divide by (n-1) = 40 / (5-1) = 40/4 = 10

In this example, the sample variance is 10.

**Conclusion**

Calculating sample variance is essential for determining how spread out a data set is and understanding its overall variability. With these step-by-step instructions and formula, you can effectively compute the sample variance for any given data set. Remember that a higher sample variance denotes a more dispersed data set, while a lower value signifies consistency among the individual observations.