A company wants to determine if there is a significant difference in the productivity levels of developers who work from home versus those who work in the office. Good thing you already know a t-test can help with it.

The company has two independent developer teams. One team works from home, and the other works from the office. You are given two files, `'work_from_home.csv'` and `'work_from_office.csv'`. They contain the completed tasks count per month for each developer.

Your task is to conduct a t-test. The company wants to know whether developers who work from the office are more productive than home workers. If so, they will also force the second team to work from the office. In case of home workers being more productive, the company will not make any changes. So the desired alternative hypothesis is "The office worker's mean is greater than the home worker's".

Let's check if the variances are the same.

import pandas as pd

home_workers = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/work_from_home.csv').squeeze()
office_workers = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/work_from_office.csv').squeeze()
# Printing sample standard deviations
print('Home workers std:', home_workers.std())
print('Office workers std:', office_workers.std())

The second standard deviation is twice as much as the first, so variances differ.
Recall the function `ttest_ind` to perform a t-test.
```python
st.ttest_ind(a, b, equal_var=True, alternative='two-sided')
```

This section will help us deal with the first real statistical case: finding confidence intervals. It requires knowledge of NumPy, pandas, Matplotlib, and Seaborn library to calculate math formulas and build visualization! To encourage you to pass this section, I want to point out that you will run across a small amount of theory but a significant amount of practice!

An inseparable part of a data analyst's life is conducting hypothesis testing. After completing this section, you will understand the idea behind testing in statistics and will be able to conduct a t-test using Python.