Q#90: Testing user conversion
Given the following dataset, can you see if there’s a significant difference between the conversion rate of users between the test and control group? The relevant columns in the table are conversion and test. The conversion column has values of 0 and 1 which represent if the user converted (1) or not (0). The test table has values of 0 and 1 as well, 0 for the control group and 1 for the test group.
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ANSWER
We got another T-Test problem again, this time to check if folks are utilizing a product/engagement. We know the drill lets use the python scipy package!
Loading the Data
We'll use the pd.read_csv function to load the dataset from the provided URL.
url = "https://raw.githubusercontent.com/erood/interviewqs.com_code_snippets/master/Datasets/test_table_truncated.csv"
data = pd.read_csv(url)Analyzing Conversion Rates
Now, let's calculate the conversion rates for both the test and control groups and then determine if there's a significant difference between them.
# Grouping data by 'test' and calculating mean conversion rate
conversion_rates = data.groupby('test')['conversion'].mean()
# Printing conversion rates
print("Conversion Rates:")
print(conversion_rates)T-test Hypothesis Testing
To determine if there's a significant difference in conversion rates between the test and control groups, we'll perform a hypothesis test. We'll use a two-sample t-test assuming unequal variances since we want to compare means of two independent groups.
# Extracting data for test and control groups
test_group = data[data['test'] == 1]['conversion']
control_group = data[data['test'] == 0]['conversion']
# Performing t-test
t_stat, p_value = stats.ttest_ind(test_group, control_group, equal_var=False)
# Printing t-statistic and p-value
print("T-statistic:", t_stat)
print("P-value:", p_value)Interpreting the Results
The p-value obtained from the t-test indicates the probability of observing the obtained difference in conversion rates (or a more extreme difference) if there were no true difference between the test and control groups. A lower p-value suggests stronger evidence against the null hypothesis.
We can set a significance level (e.g., 0.05) to determine whether the difference is statistically significant. If the p-value is less than the chosen significance level (which it is for us), we reject the null hypothesis and conclude that there is a significant difference in conversion rates between the groups.
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