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pvalue_inspector\n\nThis `README.md` was created using my package [README_genie](https://github.com/browshanravan/README_genie).\n\nA Streamlit-based application for performing two-sample statistical significance tests (t-tests and Mann-Whitney U) on data stored in CSV files.\n\n---\n\n## About This Project\n\n**pvalue_inspector** provides an interactive web UI where users can upload a CSV file, select two columns of interest, specify assumptions about variance and distribution, and instantly compute:\n\n- Standard (Student’s) t-test  \n- Welch’s t-test  \n- Mann-Whitney U test  \n\nResults include the test name, p-value, and a simple null-hypothesis decision (Reject/Accept at α=0.05).\n\n---\n\n## Project Description\n\nThis tool is ideal for:\n\n- Quick, exploratory hypothesis testing  \n- Teaching/demoing basic inferential statistics  \n- Lightweight, no-code statistical analysis  \n\nKey features:\n\n- Automatic handling of equal/unequal sample sizes (drops NaNs when needed)  \n- Choice of parametric (t-tests) or non-parametric (Mann-Whitney U) methods  \n- Streamlined, three-step workflow: upload → select columns → choose test  \n- Runs entirely in the browser via Streamlit—no web-dev expertise required  \n\n---\n\n## Getting Started\n\n### Prerequisites\n\n- Python 3.10 or higher\n- pip  \n\n\u003e Optionally, use the included Dev Container (VS Code + Docker) to spin up a ready-to-code environment.\n\n### Installation\n\n1. Clone the repository  \n   ```bash\n   git clone https://github.com/browshanravan/pvalue_inspector.git\n   cd pvalue_inspector\n   ```\n\n2. Install Python dependencies  \n   ```bash\n   pip install -r requirements.txt\n   ```\n\n### Dev Container Setup (VS Code)\n\n1. Open this folder in VS Code.  \n2. Allow the “Dev Container” prompt to rebuild.  \n3. A container with Python 3.10 and all tools will be provisioned automatically.\n\n---\n\n## Quickstart\n\nOption 1: Run the helper script  \n```bash\nsh app.sh\n```\nThis will install dependencies and launch the app on `localhost:8501`.\n\nOption 2: Manual launch  \n```bash\npip install -r requirements.txt\nstreamlit run main.py --server.port 8501\n```\n\nThen open your browser at:  \n```\nhttp://localhost:8501\n```\n\n---\n\n## Usage\n\n1. **Upload a CSV**  \n2. **Select two columns** from the dropdown  \n3. If **no missing values**, choose:\n   - “Are you assuming equal population variance?”  \n   - “Are you assuming equal population distribution?”  \n4. Select one of:\n   - **Standard t-test** (equal variance)  \n   - **Welch’s t-test** (unequal variance)  \n   - **Mann-Whitney U** (non-parametric or unequal lengths)  \n5. View test statistic, p-value, and a “Rejected”/“Accepted” null-hypothesis outcome.\n\n---\n\n## Project Structure\n\n```\n.\n├── app.sh                      # Install \u0026 launch script\n├── main.py                     # Streamlit application\n├── requirements.txt            # Python dependencies\n├── LICENSE                     # MIT License\n├── .streamlit/config.toml      # Streamlit UI/server config\n├── .devcontainer/              # VS Code Dev Container config\n│   ├── Dockerfile\n│   └── devcontainer.json\n└── pvalue_inspector/           # Python package\n    └── src/\n        └── utils.py            # CSV‐reading utility\n```\n\n---\n\n## Contributing\n\nContributions, issues and feature requests are welcome. Feel free to fork the repository and submit a pull request.\n\n---\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrowshanravan%2Fpvalue_inspector","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbrowshanravan%2Fpvalue_inspector","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrowshanravan%2Fpvalue_inspector/lists"}