**Variance** measures how much the values in a dataset deviate from the mean. It provides insight into the spread or dispersion of the data. To better understand this concept, examine the distribution of salaries in the dataset. 

The formula for calculating variance differs depending on whether you're working with a sample or an entire population. In this chapter, the focus is on calculating **population variance**.

### Explanation
Population variance is calculated by summing the squares of the differences between each data point and the population mean, and then dividing by the number of elements in the population.

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.

Learning Statistics with Python

Population Variance

Explanation