Have you ever wondered how often you usually use the word probability? It is time to dive deeper into this very familiar term!

>Please take a look at the simplest example: imagine that you toss a coin, it is always a **50%** chance of getting **head** or **tail**. I mean that there are only *two possible outcomes*, and the probability for each case is **1/2** (1/2 equal to 50 %).

It is time to turn to the definition of the **random variable** and clarify how we received such numbers. 

 The **random variable** is a quantity that equals one and only one value depending on the result of testing result.

Let's assume that **X** is the **random variable** with several outcomes (getting head or tail); therefore, we can mark them as **x1** and **x2**.

The formula for **calculating the probability** is the following: 

Let's build a small table for the probability distribution of random variables:

I hope you're interested in learning probability theory now! Here, it would be best to memorize the formula written above and the definition of a random variable. 

Indeed, each event can result either in success or failure. For instance, if you have a box with balls colored differently (red or green) and want to put a red one, your event has two outcomes: red ball or not a red ball. 

The picture for the task:

It's time to dive deeper into the probability theory. Here we will learn basic formulas. The main idea of the section is to help us to understand the term probability using real-life examples. 

Here, we will get acquainted with the rules in probability theory. This section will be more complicated than the previous one. It will be attractive because we are going to work with the real challenges. But it should be noted that we learn some formulas.

Doing experiments is always funny, but here we will combine exciting tasks with programming learning. In this section, we will work with the binom object to conduct experiments in Python.

Distribution is a key to probability learning, but don't be petrified by this term. This section provides you with real-life explanations and relevant formulas. 

The normal distribution is the most commonly used one. Here we will work with the distribution with the help of real-life challenges.