The Art of A/B Testing

The Art of A/B Testing

0%

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

Descriptive Statistics

Before moving on to visualizing the distribution, it makes sense to look at the descriptive statistics of each parameter in the dataset.

Among the key parameters we need are the following:

Number of observations;
Average value;
Standard deviation;
Median;
Minimum value;
Maximum value.

Let's get on with it. We have the results of a controlled experiment for two groups of users.

Preliminary A/A testing showed that the experiment was adequate. Let's display our files:

In this table, we have 4 columns:

'Impression' - the number of views of the product page; 'Click' - the number of transitions to the product page; 'Purchase' - the number of product purchases; 'Earning' - profit from the sale of the product.

Now let's calculate the descriptive statistics and display them on the screen:

We use the .agg() method for the convenience of calculating descriptive statistics. This method is called aggregation. Aggregations are a way of collapsing, summarizing, or grouping data. Also, we use the .concat() method to conveniently display the aggregation results on the screen.

The averages seem pretty close. Or not?

Everything was clear?

Section 2. Chapter 2

some-alt