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

Course Content

The Art of A/B Testing

The First T-Test

The t-test provides two main results:

t-statistic. This is the calculated measure of the significance of the difference between the group means. A higher t-statistic value indicates a larger difference between the groups.
p-value. It represents the probability of obtaining the observed or even larger difference between the groups if the actual difference is zero (i.e. if the null hypothesis is true). A smaller p-value suggests that it is less likely for such a large difference to occur by chance alone and provides more evidence in favor of the alternative hypothesis of a difference between the groups. Typically, if the p-value is less than 0.05, we consider the difference statistically significant.

The t-test is widely used in scientific research, medicine, economics, and other fields to determine the statistical significance of differences between groups.

Let's formulate hypotheses:

H₀: The mean values of the 'Impression' column in the control group and the test group are not different.

Hₐ: The mean value of the 'Impression' column in the control group is different from the mean value in the test group, indicating a statistically significant difference between the groups.

The result indicates that there is a statistically significant difference between the two groups in terms of their mean values.

The p-value is smaller than the specified level of significance.

This suggests rejecting the H₀ of no difference between the groups.

The negative value of the t-statistic may indicate that the mean value in the first group is smaller than in the second group. Now it's your turn to perform your first t-test.

Everything was clear?

Section 4. Chapter 2

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