Summary  
Using SciPy’s statistics module to calculate a confidence interval for a sample mean by applying the normal distribution’s interval method with specified confidence level, sample mean, and standard error.

General domain of usage  
Statistical inference

## What Values Can We Estimate Using a Confidence Interval?

In this course, we will estimate mean values, but you can also estimate other statistics such as variances, mathematical expectations, and more.

Explore the function used to calculate confidence intervals.


```python
st.norm.interval(confidence=0.95, loc=np.mean(dist), scale=st.sem(dist))
```

The `st.norm.interval()` function is used to compute a confidence interval with the following parameters:
- The `confidence` parameter represents the **confidence level**;
- The `loc` parameter signifies the **mean value of the distribution**;
- The `scale` is the **standard error of the mean**.

## What Is Standard Error of the Mean?

The standard error of the mean, often called standard error, measures how likely the population mean is to deviate from a sample mean.

Try to change the `confidence` 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(confidence=0.95, loc=np.mean(dist), scale=st.sem(dist))

print(confidence)

Build a strong foundation in statistics using Python. Learn essential statistical concepts and apply them through NumPy and pandas. Move from basic measures like mean and variance to hypothesis testing, confidence intervals, and data-driven insights with hands-on practice.

Discover core statistical principles, including data types, measures of central tendency, and key differences between samples and populations.

Learn to calculate and interpret mean, median, and mode using Python. Practice these operations with pandas to analyze real datasets.

Understand how variance and standard deviation measure data spread. Learn to compute both manually and using Python tools.

Explore how covariance and correlation describe relationships between variables. Practice calculating and comparing both metrics in Python.

Master confidence intervals to estimate population parameters. Use NumPy, pandas, and visualization libraries to calculate and interpret intervals with real data.

Learn the fundamentals of hypothesis testing and the t-test. Understand how to design, perform, and interpret tests using Python to support data-driven decisions.

Calculating Confidence Interval with Python

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Calculating Confidence Interval with Python