ab-test-stats

guia-matthieu

更新于 2 days ago

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测试testingdesigndata

关于

This skill calculates statistical significance for A/B tests, helping developers determine if results are meaningful. It assists with sample size planning, test duration estimation, and power analysis for conversion experiments. Use it to make data-driven decisions by analyzing test outcomes and ensuring reliable experiment design.

快速安装

Claude Code

技能文档

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

GitHub 仓库

guia-matthieu/clawfu-skills

路径: skills/analytics/ab-test-stats

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