| name | hypothesis-testing-and-ab-evaluation |
| description | [TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables. |
| version | 1.0.0 |
| tags | ["data-science",["TODO"]] |
| metadata | {"skill-author":"Marie-Lynne Block"} |
What this skill does
[TODO] Define the specific workflow this skill standardises, including default libraries,
quality checks, and expected deliverables.
When to use it
[TODO] List concrete user intents and trigger phrases that should activate this skill.
Instructions
- Clarify the objective, data assumptions, and success metrics.
- Execute a leakage-safe and reproducible workflow for this skill domain.
- Validate outputs with diagnostics, edge-case checks, and documented caveats.
Output format
- A concise plan of action
- Executable code or commands
- Validation summary with assumptions and risks
Examples
Example 1 - baseline workflow
Input: User asks for help in hypothesis-testing-and-ab-evaluation.
Expected output: A reproducible, validated workflow using the skill's core tools.
Notes
- Prefer documented, stable APIs over experimental shortcuts.
- Record assumptions explicitly when data quality or labels are uncertain.
