The 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')
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)
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!
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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')
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)
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!
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