Summary  
This chapter explains how to calculate the sample variance of a dataset—using a formula that adjusts for sample size—and how to interpret the result as a measure of data dispersion.  

General domain of usage  
Statistical data analysis

Now it's time to explore **sample variance**. While the concept is similar to **population variance**, the formula is slightly different to account for the fact that the data represents only a subset of the entire population.

**Formula**

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**.
>Note
>
>The greater the variance, the more the values are scattered.

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

This course gives you a basic knowledge of statistics. It is intended for students with basic knowledge of Python syntax as they will be working with NumPy and pandas modules. During the course, you will become familiar with the basic concepts and then gradually move on to more complex concepts and tasks.

This section will help us deal with the first real statistical case: finding confidence intervals. It requires knowledge of NumPy, pandas, Matplotlib, and Seaborn library to calculate math formulas and build visualization! To encourage you to pass this section, I want to point out that you will run across a small amount of theory but a significant amount of practice!

An inseparable part of a data analyst's life is conducting hypothesis testing. After completing this section, you will understand the idea behind testing in statistics and will be able to conduct a t-test using Python.

Sample Variance

Variance Key Note

Awesome!

Sample Variance

Variance Key Note