Let's perform hypothesis testing using the stats library. Let's consider the following scenario.

In a study about mental health in youth, 48% of parents believed that social media was the cause of their teenagers' stress:

• Population: Parent with a teenager (age >= 18)
• Parameter of interest: p
• Null hypothesis: p = 0.48
• Alternative hypothesis: p > 0.48

Data: 4,500 people were surveyed, and 65% of those who were surveyed believed that their teenagers' stress is due to social media.

Let's start the hypothesis testing:

1. First, import the required libraries:

import statsmodels.api as sm
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

2. Next, let's declare the variables:

n = 4500
pnull= 0.48
phat = 0.65

3. Now, we can use the proportions_ztest method to calculate the new P-value. Check out the following snippet:

sm.stats.proportions_ztest(phat * n, n, pnull, alternative='larger')

The output of the preceding code is as follows:

(23.90916877786327, 1.2294951052777303e-126)

Our calculated P-value of 1.2294951052777303e-126 is pretty small, and we can reject the null hypothesis, which is that social media is the cause of teenagers' stress.