We have received the **p-value** that equals the number that ≤ **0.05**:

  - Traditionally, if p ≤ 0.05, then there are enough arguments to reject the H0 and approve H1, although there is a small chance against this. Then one can reject the H0 and say that the results are significant at the 5% level;
  - On the contrary, if p > 0.05, then there are not enough arguments to reject the H0. Without rejecting the H0, we can state that the results are not significant at the 5% level. 

So, we can **approve the H1** hypothesis.
It means that the new version of the site can be better, and we can implement it to increase in clicks.

A/B test is one of the critical decisions when implementing something new in the IT sphere. It is the right time to get acquainted with this test!

In this section, we will learn some preparation things that are held before the A/B test.

In this section, we will perform our first A/B test. An easy version of the A/B test will help us to practice without difficulties!

It is time to meet the real A/B test. In this section, we will train ourselves using the knowledge we received during the two previous sections. After studying this section, you can say: 'I know what the A/B test is!'.

To Sum Up