Knowing statistics will broaden your horizons; you will be able to find statistical patterns and manipulating data. Let's start with the fundamental definitions: **sample** and **population**.

**Sample**

A sample is an unbiased subset of the population that contains characteristics of the population.

**Population**

The population is a set of identical items in our study of interest

**Sample vs Population: Example**

||**Population**|**Sample**|
|---|---|---|
|**Example:**|All the students in the class|The top 5 students in the class|
|**Example:**|All students graduated from university|100 random students graduated from university|

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.

	Population	Sample
Example:	All the students in the class	The top 5 students in the class
Example:	All students graduated from university	100 random students graduated from university