This repository is dedicated to the practical implementation of statistical tests within data analysis workflows. It provides ready-to-use Python scripts and Jupyter notebooks that apply both descriptive and inferential statistics to real and simulated datasets. Each project is crafted to demonstrate how to use statistical methods to explore, summarize, and make data-driven decisions. Whether analyzing group differences, testing relationships, or validating assumptions, this repository bridges theoretical concepts with applied data science.
The main goal is to simplify the use of statistical testing in data analysis by offering reusable and interpretable code for common scenarios. It empowers users to understand when and how to apply statistical tests, interpret the results correctly, and use them to support sound conclusions. This resource serves both as a learning tool and as a reference point for performing reliable and accurate statistical analysis.
- Python 3.x
- Pandas – for data wrangling and exploration
- NumPy – for numerical operations
- SciPy (scipy.stats) – for implementing statistical tests
- Statsmodels – for modeling and inferential statistics
- Matplotlib & Seaborn – for data visualization
- Jupyter Notebooks – for interactive execution and annotation