Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
The Third T-Test | T-Test
The Art of A/B Testing
course content

Course Content

The Art of A/B Testing

The Art of A/B Testing

1. What is A/B testing?
2. Normality Check
3. Variances in A/B Testing
4. T-Test
5. U-Test

bookThe Third T-Test

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

Levene's Test:

1234567891011121314151617181920
# 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')
copy

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

H₀: The mean values of the column do not differ between the groups.

Hₐ: The mean values of the column differ between the groups.

123456789101112131415161718
# 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)
copy

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!

Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 4. Chapter 4
We're sorry to hear that something went wrong. What happened?
some-alt