| name | A/B Test Statistical Analyzer |
| description | Performs statistical analysis for A/B testing experiments |
| version | 1.0.0 |
| category | Analytics |
| skillId | SK-DEA-014 |
| allowed-tools | ["Read","Write","Edit","Glob","Grep","Bash"] |
| graph | {"domains":["domain:data-engineering"],"specializations":["specialization:data-engineering-analytics"],"skillAreas":["skill-area:statistical-analysis","skill-area:data-analytics"],"roles":["role:analytics-engineer","role:data-analyst"],"workflows":["workflow:experiment-design"]} |
A/B Test Statistical Analyzer
Overview
Performs statistical analysis for A/B testing experiments. This skill provides rigorous statistical methods to determine experiment validity and significance.
Capabilities
- Sample size calculation
- Statistical significance testing
- Bayesian analysis
- Sequential testing
- Multi-armed bandit analysis
- Segment analysis
- Novelty/primacy effect detection
- SRM (Sample Ratio Mismatch) detection
- Confidence interval calculation
- Power analysis
Input Schema
{
"experimentData": {
"control": "object",
"variants": ["object"]
},
"metrics": [{
"name": "string",
"type": "conversion|continuous|ratio"
}],
"analysisType": "frequentist|bayesian|sequential"
}
Output Schema
{
"results": [{
"metric": "string",
"controlValue": "number",
"variantValues": ["number"],
"pValue": "number",
"confidenceInterval": "object",
"significant": "boolean"
}],
"srmCheck": "object",
"recommendation": "string"
}
Target Processes
- A/B Testing Pipeline
- Feature Store Setup
Usage Guidelines
- Provide complete experiment data for control and variants
- Define metrics with appropriate types
- Select analysis methodology based on requirements
- Review SRM checks before interpreting results
Best Practices
- Always check for sample ratio mismatch before analysis
- Use appropriate statistical tests for metric types
- Consider practical significance alongside statistical significance
- Account for multiple comparison corrections
- Document assumptions and limitations