ab-test-dashboard
starsforks
How to install ab-test-dashboard?
nguyenphp/antigravity-marketing/ab-test-dashboard
$npx -y skills add nguyenphp/antigravity-marketing --skill ab-test-dashboardBuild and analyze A/B test dashboards, calculate statistical significance, and track experiment results.
SKILL.md
| 1 | # A/B Test Dashboard |
| 2 | |
| 3 | > Track experiments, measure results, and make data-driven decisions. |
| 4 | |
| 5 | --- |
| 6 | |
| 7 | ## 🎯 What This Skill Does |
| 8 | |
| 9 | | Capability | Description | |
| 10 | |------------|-------------| |
| 11 | | **Design Tests** | Structure proper A/B experiments | |
| 12 | | **Track Results** | Monitor test performance | |
| 13 | | **Calculate Stats** | Statistical significance checks | |
| 14 | | **Visualize Data** | Dashboard creation | |
| 15 | | **Report Findings** | Communicate results clearly | |
| 16 | |
| 17 | --- |
| 18 | |
| 19 | ## 1. A/B Test Basics |
| 20 | |
| 21 | ### What is an A/B Test? |
| 22 | |
| 23 | | Term | Meaning | |
| 24 | |------|---------| |
| 25 | | **Control (A)** | Your current version | |
| 26 | | **Variant (B)** | The change you're testing | |
| 27 | | **Sample size** | Number of visitors needed | |
| 28 | | **Significance** | Confidence results aren't random | |
| 29 | | **Conversion** | The action you're measuring | |
| 30 | |
| 31 | ### What to Test (Priority) |
| 32 | |
| 33 | | High Impact | Medium Impact | Low Impact | |
| 34 | |-------------|---------------|------------| |
| 35 | | Headlines | Button text | Font size | |
| 36 | | CTAs | Images | Colors | |
| 37 | | Pricing | Form fields | Spacing | |
| 38 | | Offers | Layout | Icons | |
| 39 | | Landing pages | Navigation | Animations | |
| 40 | |
| 41 | --- |
| 42 | |
| 43 | ## 2. Sample Size Calculator |
| 44 | |
| 45 | ### Quick Formula |
| 46 | |
| 47 | ``` |
| 48 | Sample Size = (Z² × p × (1-p)) / E² |
| 49 | |
| 50 | Where: |
| 51 | - Z = 1.96 for 95% confidence |
| 52 | - p = expected conversion rate |
| 53 | - E = margin of error (±) |
| 54 | ``` |
| 55 | |
| 56 | ### Simple Reference Table |
| 57 | |
| 58 | | Current Rate | Lift to Detect | Sample per Variation | |
| 59 | |--------------|----------------|----------------------| |
| 60 | | 1% | 20% | ~40,000 | |
| 61 | | 2% | 20% | ~20,000 | |
| 62 | | 5% | 20% | ~8,000 | |
| 63 | | 10% | 15% | ~4,000 | |
| 64 | | 20% | 10% | ~3,000 | |
| 65 | |
| 66 | ### Online Calculators |
| 67 | |
| 68 | | Tool | URL | |
| 69 | |------|-----| |
| 70 | | **Optimizely** | optimizely.com/sample-size-calculator | |
| 71 | | **AB Tasty** | abtasty.com/sample-size-calculator | |
| 72 | | **Evan Miller** | evanmiller.org/ab-testing/sample-size | |
| 73 | |
| 74 | --- |
| 75 | |
| 76 | ## 3. Dashboard Metrics |
| 77 | |
| 78 | ### Core Metrics to Track |
| 79 | |
| 80 | | Metric | Formula | |
| 81 | |--------|---------| |
| 82 | | **Conversion Rate** | Conversions / Visitors × 100 | |
| 83 | | **Relative Lift** | (B - A) / A × 100 | |
| 84 | | **Confidence Level** | Statistical p-value calculation | |
| 85 | | **Visitors per Day** | Total visitors / Days running | |
| 86 | | **Days to Significance** | Required sample / Daily visitors | |
| 87 | |
| 88 | ### Dashboard Layout |
| 89 | |
| 90 | ``` |
| 91 | ┌─────────────────────────────────────────────┐ |
| 92 | │ TEST: [Name] Status: RUNNING │ |
| 93 | ├─────────────────────────────────────────────┤ |
| 94 | │ │ |
| 95 | │ Control (A) Variant (B) │ |
| 96 | │ ┌─────────┐ ┌─────────┐ │ |
| 97 | │ │ 2.5% │ │ 3.1% │ │ |
| 98 | │ │ Conv │ │ Conv │ │ |
| 99 | │ └─────────┘ └─────────┘ │ |
| 100 | │ │ |
| 101 | │ Visitors: 5,432 Visitors: 5,489 │ |
| 102 | │ Conversions: 136 Conversions: 170 │ |
| 103 | │ │ |
| 104 | │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ │ |
| 105 | │ Lift: +24% Confidence: 87% │ |
| 106 | │ ⚠️ Not yet significant (need 95%) │ |
| 107 | │ │ |
| 108 | ├─────────────────────────────────────────────┤ |
| 109 | │ Progress: ████████░░░░░░ 68% complete │ |
| 110 | │ Est. completion: 4 more days │ |
| 111 | └─────────────────────────────────────────────┘ |
| 112 | ``` |
| 113 | |
| 114 | --- |
| 115 | |
| 116 | ## 4. Statistical Significance |
| 117 | |
| 118 | ### What Does It Mean? |
| 119 | |
| 120 | | Confidence | Meaning | |
| 121 | |------------|---------| |
| 122 | | **95%** | 5% chance results are random (standard) | |
| 123 | | **99%** | 1% chance results are random (high stakes) | |
| 124 | | **90%** | 10% chance results are random (quick tests) | |
| 125 | |
| 126 | ### Quick Significance Check |
| 127 | |
| 128 | | Scenario | Likely Significant? | |
| 129 | |----------|---------------------| |
| 130 | | Big difference + many visitors | ✅ Yes | |
| 131 | | Small difference + few visitors | ❌ No | |
| 132 | | Big difference + few visitors | ⚠️ Wait | |
| 133 | | Small difference + many visitors | ⚠️ Maybe real, but small | |
| 134 | |
| 135 | ### Avoid Common Mistakes |
| 136 | |
| 137 | | Mistake | Problem | Solution | |
| 138 | |---------|---------|----------| |
| 139 | | Stopping early | False positives | Wait for sample size | |
| 140 | | Peeking too much | Bias decisions | Set check schedule | |
| 141 | | Testing too many things | Can't attribute cause | One change at a time | |
| 142 | | Running too short | Misses weekly patterns | Run 1-2 full weeks | |
| 143 | |
| 144 | --- |
| 145 | |
| 146 | ## 5. Simple Dashboard with Google Sheets |
| 147 | |
| 148 | ### Setup Steps |
| 149 | |
| 150 | ``` |
| 151 | 1. Create new Google Sheet |
| 152 | 2. Tab 1: Raw data (date, variant, visitors, conversions) |
| 153 | 3. Tab 2: Summary calculations |
| 154 | 4. Tab 3: Charts |
| 155 | ``` |
| 156 | |
| 157 | ### Formula Examples |
| 158 | |
| 159 | | Metric | Google Sheets Formula | |
| 160 | |--------|----------------------| |
| 161 | | **Conversion Rate A** | `=SUMIF(B:B,"A",D:D)/SUMIF(B:B,"A",C:C)` | |
| 162 | | **Conversion Rate B** | `=SUMIF(B:B,"B",D:D)/SUMIF(B:B,"B",C:C)` | |
| 163 | | **Lift** | `=(B_Rate-A_Rate)/A_Rate` | |
| 164 | | **Daily Visitors** | `=SUMIF(A:A,TODAY()-1,C:C)` | |
| 165 | |
| 166 | ### Simple Significance Formula |
| 167 | |
| 168 | ``` |
| 169 | For approximation (use proper calculator for real tests): |
| 170 | |
| 171 | Z = (pB - pA) / SQRT(p*(1-p)*(1/nA + 1/nB)) |
| 172 | |
| 173 | Where: |
| 174 | - pA, pB = conversion rates |
| 175 | - p = pooled |
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