/Hypothesis-Testing-in-Data-Analysis

Hypothesis Testing in Data Analysis This repository contains a Jupyter Notebook demonstrating various hypothesis testing techniques using Python. It covers statistical tests such as t-tests, ANOVA, chi-square, and non-parametric methods, with real-world examples and visualizations.

Primary LanguageJupyter Notebook

Hypothesis Testing in Data Analysis

Overview

This Jupyter Notebook provides a detailed exploration of hypothesis testing, a fundamental statistical method used to infer conclusions about populations based on sample data. The notebook includes theoretical explanations, step-by-step implementations, and real-world examples.

Key Topics Covered

  • Introduction to Hypothesis Testing

    • Null and Alternative Hypotheses
    • Type I and Type II Errors
    • Significance Levels & p-values

  • Types of Hypothesis Tests

    • One-sample and Two-sample t-tests
    • Paired t-tests
    • ANOVA (Analysis of Variance)
    • Chi-square test
    • Non-parametric tests (e.g., Wilcoxon, Mann-Whitney U)

  • Implementation in Python

    • Using scipy.stats for hypothesis testing
    • Visualization of test results using matplotlib and seaborn
    • Interpretation of statistical outputs

Requirements

To run the notebook, install the required Python libraries:

pip install numpy pandas scipy matplotlib seaborn

How to Use

  1. Open the Jupyter Notebook: 
    jupyter notebook Hypothesis_Testing.ipynb
  2. Run the cells sequentially to explore different hypothesis testing techniques.
  3. Modify the dataset or parameters to experiment with different statistical tests.

Applications

This notebook can be useful for:

  • Data scientists & analysts performing A/B testing and statistical inference.
  • Students & researchers learning about hypothesis testing methodologies.
  • Business analysts making data-driven decisions based on statistical tests.

Author

Raghav Tigadi
Master’s in Computer Science, NJIT

License

This project is licensed under the MIT License.