To perform a t-test in Python, you only need to choose the alternative hypothesis and whether variances are homogeneous(roughly equal).  
The `ttest_ind()` function from `scipy.stats` takes care of everything else. Here is its syntax:
```python
st.ttest_ind(a, b, equal_var = True, alternative='two-sided')
```

Parameters:
- `a` — first sample;
- `b` — second sample;
- `equal_var` — set `True` if variances are roughly equal and `False` if they aren't;
- `alternative` — type of alternative hypothesis:
   - `'two-sided'` — means are not equal;
   - `'less'` — the first mean is less than the second;
   - `'greater'` — the first mean is greater than the second.

Returns:
 - `statistic` — the value of **t** statistic;
- `pvalue` — the p-value.

We are interested in the `pvalue`. If it is lower than **α**(usually 0.05), then the **t** statistic is in the critical region, so we should accept the alternative hypothesis. And if `pvalue` is greater than **α** — we accept the null hypothesis that means are equal.  
Here an example of applying t-test to our heights dataset:

import pandas as pd
import scipy.stats as st

# Load the data
male = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/male.csv').squeeze()
female = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/female.csv').squeeze()

# Apply t-test
t_stat, pvalue = st.ttest_ind(male, female, equal_var=True, alternative="greater")

if pvalue > 0.05:
# Check if we should support or not the null hypothesis if pvalue > 0.05:
    print("We support the null hypothesis, the mean values are equal")
else:
    print("We reject the null hypothesis, males are taller")

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.