A dynamic statistical companion that adapts to any level of expertise to build intuition, ensure analytical rigor, and prevent common data misconceptions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install statistics
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 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 Statistics?
The Statistics skill is a sophisticated framework within the Openclaw Skills ecosystem designed to assist users in navigating the complexities of data analysis. It functions by detecting the user's technical level—ranging from beginners to seasoned researchers—and adjusting its pedagogical approach accordingly. By prioritizing intuition and visualization before diving into formulas, it ensures that users don't just calculate numbers but actually understand the underlying stories they tell.
For researchers and students, this skill acts as a rigorous filter, demanding explicit statements of assumptions and distinguishing between exploratory and confirmatory data analysis. It bridges the gap between theoretical probability and practical application, making it an essential tool for anyone looking to make data-driven decisions with confidence and integrity.
Statistics Use Cases
- Building foundational intuition for beginners using physical metaphors like dice and coin flips.
- Guiding students through the selection of appropriate statistical tests and validating their underlying assumptions.
- Assisting researchers in preventing p-hacking through pre-registration and power analysis frameworks.
- Helping educators clarify common misconceptions such as the difference between p-values and hypothesis probability.
- Implementing rigorous causal inference models using Directed Acyclic Graphs (DAGs) and potential outcomes.
How Statistics Works
- Level Detection: The skill analyzes the user's input for notation familiarity and technical software mentions to determine the appropriate complexity level.
- Visualization First: Before any computation, the skill prompts for data visualization to identify shapes, spreads, and outliers.
- Assumption Validation: It systematically checks for normality, independence, and equal variance before recommending specific statistical tests.
- Dual Narrative Reporting: It generates results that include both statistical significance (p-values) and practical importance (effect sizes/confidence intervals).
- Scientific Integrity Check: For advanced workflows, it enforces a distinction between exploratory data mining and confirmatory hypothesis testing.
Statistics Setup
To deploy the Statistics skill within your environment, ensure you are using an agent compatible with Openclaw Skills. The skill supports Linux, Darwin (macOS), and Win32 platforms.
# Example command to initialize the statistics skill context
claw install skill-statistics
# To verify the skill is active in your current session
claw skill list --active
Statistics Data Schema & Taxonomy
The Statistics skill organizes information based on the level of technical depth required and the specific phase of the analytical lifecycle.
| Component | Description | Metadata Key |
|---|---|---|
| User Profile | Detects level (Beginner, Student, Researcher, Teacher) | context_level |
| Test Suite | Catalog of tests (t-test, ANOVA, Regression) and assumptions | test_framework |
| Diagnostics | Residual plots, linearity checks, and bias assessments | diagnostic_logs |
| Philosophy | Bayesian vs. Frequentist approach parameters | logic_model |
Statistics Advanced Features
- Adaptive Notation: Automatically switches between Roman letters for sample statistics and Greek letters for population parameters based on user level.
- Simulation Tools: Supports intuition building through bootstrap and permutation tests to visualize what formulas often hide.
- Research Rigor Module: Includes specific hooks for power analysis, multiple comparison adjustments (Bonferroni, FDR), and missing data mechanism treatments.
- Misconception Guardrails: Active monitoring to prevent common errors like confusing correlation with causation or misinterpreting non-significant results as evidence of absence.
SKILL.md
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