The Art of A/B Testing

The Art of A/B Testing

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1. What is A/B testing?

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!

Prepare for the Test

2. Normality Check

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!

About Normality

Descriptive Statistics

Challenge: Descriptive Stats

Histograms and Box Plots

Challenge: Plotting Hists

Challenge: Plotting Boxplots

Challenge: Shapiro Test

3. Variances in A/B Testing

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!

Violin and Swarm Plots

Challenge: Violin and Swarm Plots (Part 1)

Challenge: Violin and Swarm Plots (Part 2)

Challenge: First Levene's Test

Challenge: Second Levene's Test

4. T-Test

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!

Graphical Results of Previous Studies

The First T-Test

Challenge: Second T-test

The Third T-Test

Challenge: Fourth T-Test

5. U-Test

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.

Challenge: Define metric

Challenge: Another Normality Test

The First U-Test

Challenge: The Second U-Test

Challenge: The Last U-Test

The Third U-test

Challenge: Second T-test

Task

Your task is to do a t-test. Recall that the distribution in the 'Click' column has a normal distribution in both datasets. Also, there is a statistically significant difference between the variances. Now it is time to find out whether there is statistical evidence that the means of the two samples are equal.

Remember this: We are not doing the usual T-test here, but a modification of the Welch T-test. For samples with different variances, you need to specify an additional parameter equal_var=False in the ttest_ind() function. This will lead to the use of a modification of the Welch T-test, which is designed to work with samples with different variances.

Calculate the mean values.
Do a T-Test.

Everything was clear?

Section 4. Chapter 3

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Course Content

The Art of A/B Testing

Challenge: Second T-test

Task

Your task is to do a t-test. Recall that the distribution in the 'Click' column has a normal distribution in both datasets. Also, there is a statistically significant difference between the variances. Now it is time to find out whether there is statistical evidence that the means of the two samples are equal.

Remember this: We are not doing the usual T-test here, but a modification of the Welch T-test. For samples with different variances, you need to specify an additional parameter equal_var=False in the ttest_ind() function. This will lead to the use of a modification of the Welch T-test, which is designed to work with samples with different variances.

Calculate the mean values.
Do a T-Test.

Everything was clear?

Section 4. Chapter 3

toggle bottom row

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