**Statistical Inference**

This course aims to estimate and draw conclusions about the characteristics of one or multiple populations based on samples. It introduces students to the design, implementation (using Python), and interpretation of various statistical tests to provide a foundation for selecting data analysis models.

## Prerequisites

It is necessary to have prior knowledge in Probability. Therefore, it is advisable to have a solid understanding of the following:

- Elementary Probability concepts (expectation, variance, conditional expectation, etc.).
- Probability distributions (random variables and common distributions, e.g., normal, Bernoulli, etc.).
- Convergence of distributions (law of large numbers, central limit theorem, etc.).
- Descriptive and mathematical statistics (frequency distribution, probability distribution, probability density function, cumulative distribution function, etc.).

## Educational Goals

This course is designed to provide a quick introduction to statistical inference for data engineers. As such, it meets different educational objectives:

- Understand the principles of statistical inference.
- Choose and apply statistical tests to real data.
- Interpret the result of a statistical test.
- Evaluate the reliability of a statistical test.
- Use Python to design a test.

## Course Materials

SlidesIntroduction
Statistical Tests
Tests of Conformity
Tests of Homogeneity
Paired Samples Tests
Tests of Normality
Tests of Independence