A/B Test Results Analyser

Analyse A/B test results for statistical significance

🧪 Tested by a human Advanced 👤 Analyst 👤 Marketing 👤 Product Manager
Analyse this A/B test: Control [A METRICS] vs Variant [B METRICS]. Determine: (1) Statistical significance (p-value), (2) Confidence interval, (3) Effect size, (4) Sample size adequacy, (5) Winner recommendation with caveats, (6) Whether to continue testing.

Works on

⭐ M365 Copilot (Best)

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Tips for Best Results

  • Do not call a winner too early
  • Check for novelty effect
  • Segment results by user type

📎 More Prompts

Analyse Dataset for Top Insights

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Analyse this data and give me the top [NUMBER — 3 / 5] insights. For each insight explain: what the data shows, why it matters, and what I should do about it. Include a supporting chart or visualisation for the most impactful finding. Write for [AUDIENCE — management / the team / a technical audience].
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Benchmark Comparison

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Compare our metrics against industry benchmarks. Our data: [PASTE KEY METRICS]. Industry: [INDUSTRY]. For each metric: (1) Our value, (2) Industry benchmark, (3) Percentile ranking, (4) Gap analysis, (5) Whether the gap matters and what to do about it. Flag where we significantly lag or lead.
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Break Down Results by Dimension

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Break down the results in this data by [DIMENSIONS — region, product, customer type, department]. For each segment show: total, average, percentage of whole, and trend direction. Highlight the best and worst performing segments. Create a chart comparing the top segments.
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