# EXERCISE 10.1: ANALYZING A SIMPLE A/B TEST

Objective: To demonstrate the basic principles of experimental design using an A/B test scenario in email marketing.

You are provided with data from an email marketing campaign where two different subject lines were tested to see which one yields a higher open rate. Your task is to analyze the data to determine which subject line performed better.

1. Statistical Test: Perform a t-test to see if the difference in open rates between the two groups is statistically significant.
2. Interpret Results: Based on the p-value from the t-test, conclude which subject line performed better.

Steps:

1. Import Libraries:
````1. import scipy.stats as stats`
`2. import pandas as pd````

We import two libraries: scipy.stats for statistical tests and pandas for handling data in a structured form (DataFrames).

``3. email_marketing_data = pd.read_csv(‘/data/Email_Marketing_AB_Test_Data.csv’)``

We load the data into a pandas DataFrame. This data simulates the open rates of emails for two different subject lines (Group A and Group B).

3. Separate the Data into Two Groups:
````4. group_A = email_marketing_data[email_marketing_data[‘Group’] == ‘A’][‘OpenRate’]`
`5. group_B = email_marketing_data[email_marketing_data[‘Group’] == ‘B’][‘OpenRate’]````

Here, we filter the DataFrame to create two separate series: one for each group. group_A contains the open rates for subject line A, and group_B for subject line B.

4. Perform a t-Test:
``6. t_stat, p_value = stats.ttest_ind(group_A, ...``

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