We have gathered a lot of information about our datasets. We know the **distribution** of the data and their variances. Let's review the histogram, **box plot**, and **violin plot** for the `'Impression'` column in the control and test groups.


The **Shapiro test** has provided evidence of **normality** in the distributions, and the **Levene test** has provided statistical evidence of homogeneity of **variances**. 

Now it's time to answer the main question: are the **mean** values of the control and test groups for the `'Impression'` column equal? 

As you might have guessed, we can use another statistical test for help. The <u>**t-test**</u> **(Student's t-test)** is a statistical test used to compare the means of two groups. 

It allows us to determine whether there is a statistically significant difference between the mean values of these groups. 

The **t-test** is based on the assumption that the data distribution in each group is **normal**. It also assumes homogeneity of **variances** in both groups.

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.

The Art of A/B Testing

Graphical Results of Previous Studies