Let's review the plots for the `'Purchase'` column of the control and test groups.


# Import libraries
import pandas as pd
from scipy.stats import levene

# Read .csv files
df_control = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c3b98ad3-420d-403f-908d-6ab8facc3e28/ab_control.csv', delimiter=';')
df_test = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c3b98ad3-420d-403f-908d-6ab8facc3e28/ab_test.csv', delimiter=';')

# Do Levene's test 
statistic, p_value = levene(df_control['Purchase'], df_test['Purchase'])

# Print result of Levene's test
print('Statistic:', statistic)
print('p-value:', p_value)

# Determine whether the variances are similar
if p_value > 0.05:
  print('The variances of the two groups are NOT statistically different')
else:
  print('The variances of the two groups are statistically different')

Now perform a **t-test** for the `'Purchase'` columns:

<u>H₀: The **mean** values of the column do not differ between the groups.</u>

<u>Hₐ: The **mean** values of the column differ between the groups.</u>


# Import libraries
import pandas as pd
from scipy.stats import ttest_ind

# Read .csv files
df_control = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c3b98ad3-420d-403f-908d-6ab8facc3e28/ab_control.csv', delimiter=';')
df_test = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c3b98ad3-420d-403f-908d-6ab8facc3e28/ab_test.csv', delimiter=';')

# Select only the 'Purchase' columns
data_control = df_control['Purchase']
data_test = df_test['Purchase']

# Do T-Test 
statistic, p_value = ttest_ind(data_control, data_test, equal_var=True)

# Print result of T-test
print('Statistic:', statistic)
print('p-value:', p_value)

In this case, the **p-value** (`0.350`) is higher than the acceptable significance level (`0.05`), indicating that there is insufficient evidence to suggest that the **mean** values differ between the groups. Now it's your turn!

This course is a comprehensive and practical online program designed to equip individuals with the knowledge and skills necessary to conduct effective A/B tests. 

In this course, participants will learn the fundamental principles of A/B testing, including experimental design, sample size determination, hypothesis testing, and statistical analysis.

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!

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!

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!

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!

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.

The Art of A/B Testing

The Third T-Test