Find Anomalies and Outliers

Detect unusual patterns, outliers, and data quality issues in your dataset

🧪 Tested by a human Intermediate 👤 Analyst 👤 Finance 👤 Operations
Scan this dataset for anomalies and outliers. Look for: (1) Values that are unusually high or low compared to the rest, (2) Sudden changes or spikes in trends, (3) Missing or suspicious data patterns, (4) Inconsistencies between related columns. For each finding, explain why it is unusual and suggest whether it is an error or a genuine anomaly worth investigating.

Works on

⭐ M365 Copilot (Best)

Copy & Open in

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Tips for Best Results

  • Works best with time-series data or large datasets where manual scanning is impractical
  • Add “Define an outlier as more than 2 standard deviations from the mean” for statistical precision
  • The Analyst agent can create visualisations highlighting the anomalies

📎 More Prompts

A/B Test Results Analyser

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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.
⭐ M365

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].
⭐ M365

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.
⭐ M365
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