**Which values can we estimate using a confidence interval?**

Within this course, we will estimate mean values, but you can estimate whatever you want: variances, math expectations, etc.

Let's discuss the function for finding confidence intervals.

```python
st.norm.interval(alpha=0.95, loc=np.mean(dist), scale=st.sem(dist))
```
`st.norm.interval()` - function that calculates confidence interval with the parameters:
- `alpha` parameter is the **level of confidence**,
- `loc` parameter is the **mean value of the distribution**.
- `scale` is the **value of standard error of the mean**.

**What does standard error of the mean means?**

The standard error of the mean, or standard error, demonstrates how the population mean is likely to vary from a sample mean.

Try to change the `alpha` parameter and observe the changes.

# Importing libraries
import scipy.stats as st
import numpy as np

# Creating random normal distribution
dist = st.norm.rvs(size = 1000, loc = 50, scale = 2)

# Finding confidence interval
confidence = st.norm.interval(alpha=0.95, loc=np.mean(dist), scale=st.sem(dist))

print(confidence)

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.