ab-test-stats

name	ab-test-stats
description	Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
license	MIT
metadata	{"author":"ClawFu","version":"1.0.0","mcp-server":"@clawfu/mcp-skills"}

A/B Test Statistics Calculator

Calculate statistical significance for A/B tests - know when your results are real, not random chance.

When to Use This Skill

• Test analysis - Determine if results are statistically significant
• Sample planning - Calculate required sample size before testing
• Duration estimation - Know how long to run experiments
• Power analysis - Ensure tests can detect meaningful differences

What Claude Does vs What You Decide

Claude Does	You Decide
Structures analysis frameworks	Metric definitions
Identifies patterns in data	Business interpretation
Creates visualization templates	Dashboard design
Suggests optimization areas	Action priorities
Calculates statistical measures	Decision thresholds

Dependencies

pip install scipy numpy click

Commands

Check Significance

python scripts/main.py significance --control 1000,50 --variant 1000,65
python scripts/main.py significance --control 5000,250 --variant 5000,300 --confidence 0.99

Calculate Sample Size

python scripts/main.py sample-size --baseline 0.05 --mde 0.02
python scripts/main.py sample-size --baseline 0.10 --mde 0.01 --power 0.90

Estimate Duration

python scripts/main.py duration --traffic 1000 --baseline 0.05 --mde 0.02

Examples

Example 1: Analyze Test Results

# Control: 1000 visitors, 50 conversions (5%)
# Variant: 1000 visitors, 65 conversions (6.5%)
python scripts/main.py significance --control 1000,50 --variant 1000,65

# Output:
# A/B Test Results
# ─────────────────────────
# Control:  5.00% (50/1000)
# Variant:  6.50% (65/1000)
# Lift:     +30.0%
#
# Statistical Analysis
# ─────────────────────────
# p-value:      0.089
# Confidence:   91.1%
# Result:       NOT SIGNIFICANT (need 95%)
#
# Recommendation: Continue test for more data

Example 2: Plan Sample Size

# Baseline 5% conversion, want to detect 20% relative lift (1% absolute)
python scripts/main.py sample-size --baseline 0.05 --mde 0.01

# Output:
# Sample Size Calculator
# ──────────────────────────────
# Baseline conversion: 5.0%
# Minimum detectable effect: 1.0% (20% relative)
# Target conversion: 6.0%
#
# Required per variant: 3,842 visitors
# Total required: 7,684 visitors
#
# At 1000 daily visitors: ~8 days

Key Concepts

Term	Definition
p-value	Probability result is due to chance
Confidence	1 - p-value (usually want 95%+)
Power	Probability of detecting real effect (usually 80%)
MDE	Minimum Detectable Effect - smallest lift worth detecting
Lift	Relative improvement (variant - control) / control

When Results Are Significant

p-value	Confidence	Verdict
< 0.01	> 99%	Highly Significant ✓
< 0.05	> 95%	Significant ✓
< 0.10	> 90%	Marginally Significant
≥ 0.10	< 90%	Not Significant ✗

Skill Boundaries

What This Skill Does Well

• Structuring data analysis
• Identifying patterns and trends
• Creating visualization frameworks
• Calculating statistical measures

What This Skill Cannot Do

• Access your actual data
• Replace statistical expertise
• Make business decisions
• Guarantee prediction accuracy

Related Skills

• cohort-analysis - Analyze user cohorts
• funnel-analyzer - Analyze conversion funnels

Skill Metadata

• Mode: centaur

category: analytics
subcategory: statistics
dependencies: [scipy, numpy]
difficulty: intermediate
time_saved: 3+ hours/week