Welcome to the Hypothesis Testing Tutorial repository! This tutorial covers a wide range of parametric and non-parametric hypothesis tests commonly used in data science, presented in the form of Jupyter Notebooks.
Hypothesis testing is a fundamental concept in statistics used to make inferences about a population based on sample data. It involves formulating a hypothesis about the population parameter and using statistical methods to determine whether the sample data provide enough evidence to reject or fail to reject the null hypothesis.
This tutorial covers both parametric and non-parametric hypothesis tests:
- Paired t-test: Used to compare the means of two related samples.
- ANOVA (Analysis of Variance): Used to compare the means of three or more independent groups.
- Independent t-test: Used to compare the means of two independent samples.
- Wilcoxon Signed Rank Test: Non-parametric alternative to the paired t-test for comparing two related samples.
- Friedman Test: Non-parametric alternative to repeated measures ANOVA for comparing three or more related samples.
- Mann-Whitney U Test: Non-parametric alternative to the independent t-test for comparing two independent samples.
- Kruskal-Wallis Test: Non-parametric alternative to ANOVA for comparing three or more independent groups.
Each hypothesis test is accompanied by examples to illustrate its application in real-world scenarios. The examples are presented in Jupyter Notebooks, including datasets and step-by-step explanations of the hypothesis testing process.
To get started with the tutorial, simply navigate to the directory corresponding to the hypothesis test you're interested in. Each directory contains a Jupyter Notebook with the tutorial content and example code.
Contributions to this tutorial are welcome! If you'd like to contribute improvements, additional examples, or new hypothesis tests, please feel free to submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
Happy Hypothesis Testing! 🧪✨