In the previous chapter, we created 2 plots.
We can also create 2 **confidence intervals** for these groups.

A **confidence interval** is the mean of your estimate plus and minus the variation in that estimate. This is the range of values you expect your estimate to fall between if you redo your test, within a certain level of confidence. Confidence, in statistics, is another way to describe probability.

> Tu build them use `scipy.stats.t.interval(alpha, data, loc, scale)`. In our case we will use `alpha` equals `0.95`(you may also choose `0,99`, but you will need to compare the `p-value` with `0,01` thus), the `data.shape[1]` as a `data`, `loc = data.clicks.mean()` and `scale = scipy.stats.sem(data.clicks)`.

If intervals cover each other a lot, 2 groups don't differ a lot => the new version of the site doesn't make any big changes.

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!'.

Intervals to Compare

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