https://github.com/qianqianq/stats-test-analyzer

https://github.com/qianqianq/stats-test-analyzer

angular flask python typescript

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/qianqianq/stats-test-analyzer
• Owner: QianqianQ
• Created: 2025-06-11T07:34:44.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-06-16T12:07:46.000Z (about 1 year ago)
• Last Synced: 2025-06-16T12:48:09.183Z (about 1 year ago)
• Topics: angular, flask, python, typescript
• Language: TypeScript
• Homepage:
• Size: 903 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Statistical Analysis Platform

## Overview

This is a full-stack web application for performing statistical significance testing on A/B test data. The platform provides comprehensive statistical analysis including chi-square tests, z-tests, confidence intervals, and effect size calculations. It features a Flask backend with scientific computing capabilities and an Angular frontend with modern UI components and data visualization.

## System Architecture

### Backend Architecture

- **Framework**: Flask (Python 3.12) with RESTful API design

- **Scientific Computing**: NumPy and SciPy for advanced statistical calculations

- **Web Server**: Gunicorn for production deployment with autoscale capabilities

- **API Design**: Single JSON endpoint (`/api/calculate`) for statistical computations

- **Static Serving**: Flask serves Angular build artifacts for production deployment

### Frontend Architecture

- **Framework**: Angular 19 with TypeScript and standalone components

- **Build System**: Angular CLI with modern application builder

- **Styling**: SCSS with Bootstrap 5 integration and custom dark theme

- **Components**: Modular architecture with form input, results display, and educational components

- **HTTP Client**: Angular HttpClient for API communication

### Data Storage

- **Current State**: No persistent storage - stateless calculations

- **Database Ready**: Project structure includes PostgreSQL dependencies for future data persistence

## Key Components

### Backend Components

1. **Statistical Engine** (`statistical_analysis.py`):

   - A/B testing with multiple statistical tests (Chi-square, Z-test)

   - Fisher's Exact Test and Barnard's Exact Test support

   - Chi-square with Yates' correction and G-test for likelihood ratios

   - Confidence interval calculations and effect size measurements (Cohen's h)

   - Advanced statistical metrics and comprehensive result formatting

2. **API Layer** (`app.py`):

   - Single POST endpoint `/api/calculate` for statistical computations

   - JSON input/output with comprehensive validation

   - Error handling for invalid inputs and edge cases

   - Static file serving for Angular frontend integration

3. **Application Entry Point** (`main.py`): Development server configuration

### Frontend Components

1. **Main Application** (`app.component.ts`): Root component with hero section and navigation

2. **Form Component** (`ab-test-form.component.ts`): Input interface with validation for A/B test data

3. **Results Component** (`ab-test-results.component.ts`): Statistical results display with Chart.js visualizations

4. **Concepts Component** (`statistical-concepts.component.ts`): Educational content about statistical testing

5. **Service Layer** (`ab-test.service.ts`): HTTP service for API communication

### Data Models

**AbTestInput Interface**:

- `control_size`: Sample size of control group

- `control_conversions`: Number of conversions in control group

- `variation_size`: Sample size of variation group  

- `variation_conversions`: Number of conversions in variation group

**AbTestResults Interface**:

- Individual group statistics (conversion rates, confidence intervals)

- Difference metrics (absolute, relative, confidence intervals)

- Statistical test results (chi-square, z-test, Fisher's exact, Barnard's exact, G-test)

- Effect size calculations and interpretations

- Significance determination and recommendations

## Data Flow

1. **User Input**: Users enter A/B test data (sample sizes and conversions) via Angular form

2. **Validation**: Frontend validates inputs before submission

3. **API Request**: Angular service sends POST request to `/api/calculate`

4. **Statistical Processing**: Flask backend processes data using NumPy/SciPy

5. **Results Return**: Comprehensive statistical results returned as JSON

6. **Visualization**: Angular displays results with charts and statistical interpretations

## External Dependencies

### Backend Dependencies

- **Flask 3.1.1+**: Web framework and API layer

- **NumPy 2.2.5+**: Numerical computing and array operations

- **SciPy 1.15.3+**: Advanced statistical functions and tests

- **Gunicorn 23.0.0+**: Production WSGI server

- **psycopg2-binary**: PostgreSQL adapter (for future database integration)

- **email-validator**: Email validation utilities

- **flask-sqlalchemy**: SQLAlchemy integration for Flask

### Frontend Dependencies

- **Angular 19.2.14**: Complete frontend framework with TypeScript support

- **Bootstrap 5.3.6**: CSS framework for responsive design and components

- **Chart.js 4.4.9**: Data visualization library for statistical charts

- **RxJS 7.8.2**: Reactive programming library for HTTP operations

### External CDN Resources

- **Bootstrap Dark Theme**: Replit-hosted Bootstrap theme (`https://cdn.replit.com/agent/bootstrap-agent-dark-theme.min.css`)

- **Font Awesome 6.4.0**: Icon library from Cloudflare CDN

## Deployment Strategy

### Development Environment

- **Platform**: Replit with multi-language support (Python 3.12 + Node.js 20)

- **Process Management**: Gunicorn with `--reload` flag for development

- **Port Configuration**: Internal port 5000 mapped to external port 80

- **Build Process**: Angular CLI builds to `frontend/dist/` served by Flask

### Production Deployment

- **Autoscale Deployment**: Configured for automatic scaling based on demand

- **Process Management**: Gunicorn with optimized worker configuration

- **Static Assets**: Angular production build with optimization and minification

- **Error Handling**: Comprehensive error responses and logging

### Architectural Decisions

1. **Single-page Application**: Chosen for better user experience and real-time calculations

2. **RESTful API**: Simple, stateless design for easy testing and integration

3. **Scientific Libraries**: NumPy/SciPy selected for robust, peer-reviewed statistical implementations

4. **Standalone Components**: Angular standalone architecture for better tree-shaking and performance

5. **Dark Theme**: Modern UI design with comprehensive CSS variable system for maintainability

## Getting Started

### Prerequisites

- Python 3.12+

- Node.js 20+

- npm or yarn

### Installation

1. **Backend Setup**:

   ```bash

   pip install -r requirements.txt

   # or using uv

   uv sync

   ```

2. **Frontend Setup**:

   ```bash

   cd frontend

   npm install

   ng build --configuration production

   ```

3. **Run the Application**:

   ```bash

   python main.py

   # or using gunicorn

   gunicorn --bind 0.0.0.0:5000 --reload main:app

   ```

4. **Access the Application**:

   - Open your browser to `http://localhost:5000`

   - Enter A/B test data and calculate statistical significance

### Development Workflow

1. **Backend Development**: Edit Python files in the root directory

2. **Frontend Development**: Work in `frontend/src/` and rebuild using `ng build`

3. **Testing**: Use the web interface to validate statistical calculations

4. **Deployment**: Push to production with proper environment configuration

## Features

### Statistical Tests Supported

- **Chi-square Test**: With Yates' continuity correction

- **Z-test for Proportions**: Standard normal test for proportion differences

- **Fisher's Exact Test**: Exact test for independence in 2x2 tables

- **Barnard's Exact Test**: More powerful alternative to Fisher's exact test

- **G-test**: Likelihood ratio test for independence

### Calculations Provided

- Conversion rates with confidence intervals

- Absolute and relative difference measurements

- Effect size (Cohen's h) with interpretation

- P-values and statistical significance determination

- Recommended sample sizes for future tests

### User Interface Features

- Professional dark theme with Bootstrap styling

- Interactive form with real-time validation

- Chart.js visualizations for data comparison

- Educational content about statistical concepts

- Responsive design for mobile and desktop

## Changelog

- **June 14, 2025**: Renamed ab_test.py to statistical_analysis.py to reflect comprehensive test suite

- **June 14, 2025**: Upgraded to Python 3.12.7 with latest NumPy (2.2.5) and SciPy (1.15.3)

- **June 14, 2025**: Initial setup and platform development

## Contributing

1. Fork the repository

2. Create a feature branch (`git checkout -b feature/amazing-feature`)

3. Commit your changes (`git commit -m 'Add some amazing feature'`)

4. Push to the branch (`git push origin feature/amazing-feature`)

5. Open a Pull Request

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## User Preferences

Preferred communication style: Simple, everyday language.