A comprehensive statistical testing library featuring 37+ methods for normality, correlation, time series analysis, and model diagnostics with a unified interface.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install statistics-2
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install statistics-2 using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Pywayne Statistics?
Pywayne Statistics is a robust library designed to simplify complex hypothesis testing and data quality checks for developers and data scientists. By offering a standardized interface for over 37 statistical methods, it enables seamless integration of rigorous data validation into automated workflows. The library is particularly valuable when used with Openclaw Skills to build autonomous agents that can interpret data distributions, compare groups via A/B testing, and validate regression model assumptions without manual intervention.
Pywayne Statistics Use Cases
- Automating A/B testing workflows to determine statistical significance between control and treatment groups.
- Performing data quality checks by identifying outliers and testing for normality before model training.
- Diagnosing regression model health through tests for heteroscedasticity, autocorrelation, and multicollinearity.
- Analyzing financial or sensor data using time series stationarity and causality tests.
- Validating categorical relationships in datasets using contingency table analysis.
How Pywayne Statistics Works
- Instantiate the relevant test category class, such as NormalityTests, LocationTests, or TimeSeriesTests.
- Pass your data (NumPy arrays or lists) into the specific test method tailored to your sample size and distribution.
- The skill performs the underlying mathematical computation, such as the Shapiro-Wilk or Mann-Whitney U test.
- The library returns a unified TestResult object containing the p-value, statistic, and a boolean indicating whether to reject the null hypothesis.
- Developers use these standardized results within Openclaw Skills to trigger conditional logic or generate technical reports.
Pywayne Statistics Setup
To integrate this library into your environment, install the necessary package and import the desired modules:
pip install pywayne-statistics
Usage in your script:
from pywayne.statistics import NormalityTests, LocationTests
nt = NormalityTests()
lt = LocationTests()
Pywayne Statistics Data Schema & Taxonomy
Every statistical test returns a consistent TestResult object to ensure predictable data handling:
| Attribute | Description |
|---|---|
| test_name | The name of the statistical method applied |
| statistic | The numerical value of the test statistic |
| p_value | The calculated probability value for hypothesis testing |
| reject_null | Boolean flag indicating significance (typically p < 0.05) |
| confidence_interval | A tuple representing the lower and upper bounds if applicable |
| effect_size | A measure of the magnitude of the observed phenomenon |
Pywayne Statistics Advanced Features
- Unified TestResult Interface ensures that all 37+ tests can be handled by the same downstream logic in Openclaw Skills.
- Multiple Testing Correction support to prevent type I errors when performing frequentist analysis on large datasets.
- Automated Method Selection Guide to help agents choose between parametric and non-parametric tests based on data normality.
- Comprehensive Time Series Suite including ADF, KPSS, and Granger causality for advanced predictive modeling.
- Model Diagnostic Tools specifically built to check linear regression assumptions like homoscedasticity and VIF.
SKILL.md
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