Previously, we conducted a successful **A/A testing**. To properly conduct **A/B testing**, we need to know how our data is distributed. 

There are many types of distribution in statistics, but the most recognizable type is the **Gaussian distribution** (**normal distribution**). In nature, many things are normally distributed.


IQ distribution at Kyiv Polytechnic Institute according to test results in 2020:


Weight distribution among male in the Bruges in 2019:

But there are events to which the normal distribution is alien. 
The time between emails:


The average number of customers per hour:

Different types of tests are used for different distributions. In this section, we will focus on the <u>**normal**</u> distribution and how to test for normality.

Which of the following options do you think has a **normal** distribution?

This course is a comprehensive and practical online program designed to equip individuals with the knowledge and skills necessary to conduct effective A/B tests. 

In this course, participants will learn the fundamental principles of A/B testing, including experimental design, sample size determination, hypothesis testing, and statistical analysis.

Welcome to the A/B testing course. In this section, we will tell you what controlled experiments are, where they are used, and why they are used. We will look at a specific example of a controlled experiment and talk about A/A testing. The Python programming language will help us with this. Well, let's get started!

Before conducting a controlled experiment, we need to determine the characteristics of the sample. The type of tests that we will conduct a little later will depend on these characteristics. You will learn to build distribution histograms, box plots, and check the normality of the distribution. Let's get started right now!

The last thing to do before conducting a controlled experiment is to check the sampling variances. Some types of graphs and Levene's test will help us with this. Let's talk about it in more detail in this section!

So we got to A/B testing. Soon you will make your first parametric tests, or as they are also called - T-tests. We'll use all the knowledge we've learned in the previous sections: metric definition, normality testing, variance testing, and plotting. All this will help us get statistically significant results. Well, to work!

It turns out that sometimes the samples do not have a normal distribution. This means that we cannot perform parametric testing. What to do in such a situation? Non-parametric testing. We will talk about him in the last section.

About Normality