statistics
Deterministic statistical computation for AI agents.
Agents asked to compute a mean, median, or standard deviation will often approximate or round incorrectly. On small datasets the errors are subtle. On large ones they compound. Euclid's statistics tool computes exact results on real data, so your agent's summaries and reports are verifiably correct.
Live
Operations
Examples
What it looks like in practice
Quarterly sales performance review
statistics("mean", [84200, 91500, 78300, 96100])
{ "result": "87525" }
Identifying the 90th percentile response time
statistics("percentile", [120, 340, 200, 150, 180, 95, 220, 310, 170, 190], 90)
{ "result": "313" }
Student grade distribution
statistics("median", [72, 85, 91, 68, 77, 95, 82, 88])
{ "result": "83.5" }
Manufacturing quality control
statistics("std", [10.02, 10.05, 9.98, 10.01, 9.97, 10.03])
{ "result": "0.030331..." }
Real estate price analysis
statistics("mode", [450000, 520000, 450000, 480000, 450000, 510000])
{ "result": "450000" }
Weekly calorie tracking
statistics("sum", [2100, 1850, 2300, 1950, 2050, 2400, 1900])
{ "result": "14550" }
Who uses this
Built for agents across every domain
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Product manager
An AI agent summarising A/B test results where the difference between 2.3% and 2.8% conversion determines whether a feature ships.
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HR analyst
Computing salary percentiles across departments to identify pay equity gaps in a compensation review.
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Sports analyst
An agent computing player performance statistics where a wrong standard deviation misranks draft picks.
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Quality engineer
Monitoring manufacturing tolerances. A wrong variance calculation means defective parts reach customers.
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Researcher
An AI assistant verifying descriptive statistics before a paper submission. Wrong numbers retract publications.
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Personal trainer
An AI fitness agent tracking client progress over time where accurate averages and trends matter for programming adjustments.
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