Summary  
This chapter explains how to compute sample variance by summing squared differences from the sample mean and dividing by (n–1) to measure data dispersion.

General domain of usage  
Salary distribution analysis

Now explore **sample variance**. The concept is similar to **population variance**, but the formula differs slightly because the data represents only a subset of the entire population.


**Formula**
$$
\text{variance} = \frac{\sum^n_{i=1}(x_i-\bar{x})^2}{n-1}
$$

Where:
- $$n$$ - sample size;
- $$i$$ - index of each element;
- $$x_i$$ - each element;
- $$\bar{x}$$ - sample mean value.


Look at the example of variance rounded to two decimal points for the column `'salary_in_usd'`:

The variance of the column `'salary_in_usd'` is **5034932663.18**

## Variance Key Note

The variance of the `'salary_in_usd'` column is quite substantial, indicating that the values in this column are **widely spread out**.

The greater the variance, the more the values are scattered.

Note

Select the one with lower variance from the graphs with an equal number of observations and identical mean values.

Learn core statistical concepts used in data analysis with Python. The course covers descriptive statistics, including mean, median, mode, variance, and standard deviation, as well as sampling, probability distributions, the Central Limit Theorem, and outlier detection.

Sample Variance

Variance Key Note