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.
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 'Impression' columns data_control = df_control['Impression'] data_test = df_test['Impression'] # 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)
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.
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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.
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 'Impression' columns data_control = df_control['Impression'] data_test = df_test['Impression'] # 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)
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.
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