Summary  
This chapter distinguishes between a population and a sample in statistical contexts, showing how a subset of data represents the whole.

General domain of usage  
Statistical data analysis

Understanding statistics will expand your horizons and allow you to recognize statistical patterns and work with data. The first fundamental definitions: **sample** and **population**.

**Population**: is a set of identical items in our study of interest.

Definition

**Sample**: is a subset of the population which is intended to capture the characteristics of the population (however, it might not always accurately reflect characteristics of the population due to its **quality**).

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 vs Population

Sample vs Population: Example

Awesome!

Sample vs Population

Sample vs Population: Example