Ab Test Stats

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