@gongrzhe/server-ab-test-calculator
v1.0.0
Published
A/B Test Calculator MCP App Server with statistical significance analysis
Readme
A/B Test Calculator MCP Server
Statistical significance calculator for A/B tests with confidence intervals, power analysis, and sample size recommendations.
Features
- Statistical Significance Testing - Two-proportion z-test to determine if observed differences are statistically significant
- Confidence Intervals - Wald confidence intervals for both control and variant conversion rates
- Power Analysis - Calculate current statistical power and required sample sizes for 80% power
- Relative Uplift Calculation - Quantify the percentage improvement from variant over control
- P-Value Computation - Get precise p-values to assess test results
- Flexible Confidence Levels - Support for custom confidence levels (default 95%)
Installation
npm install @gongrzhe/server-ab-test-calculatorUsage
As a CLI
npx @gongrzhe/server-ab-test-calculatorClaude Desktop Configuration
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"ab-test-calculator": {
"command": "npx",
"args": ["-y", "@gongrzhe/server-ab-test-calculator"]
}
}
}Claude Code Configuration
claude mcp add ab-test-calculator -- npx -y @gongrzhe/server-ab-test-calculatorTools
calculate-ab-test
Computes statistical significance for an A/B test including conversion rates, confidence intervals, z-score, p-value, and required sample size.
Parameters:
controlVisitors- Number of visitors in the control groupcontrolConversions- Number of conversions in the control groupvariantVisitors- Number of visitors in the variant groupvariantConversions- Number of conversions in the variant groupconfidenceLevel- Confidence level (0-1), defaults to 0.95
Returns:
- Conversion rates and confidence intervals for both groups
- Relative uplift percentage
- Z-score and p-value
- Statistical significance at the specified confidence level
- Required sample size per group for 80% statistical power
- Current power analysis
Example Prompt
Calculate significance for my A/B test: Control had 5000 visitors with 150 conversions, Variant had 5000 visitors with 195 conversions. Use 95% confidence level.License
MIT
