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KiHustle Paired T-Test API
x402
KiHustle Paired T-Test API is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-14).
Performs a paired (dependent samples) t-test on two matched numeric arrays to determine statistical significance of the difference between them
Price / call$0.002
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object with a 'result' field indicating the computed outcome and a 'status' field confirming success. In practice for a paired t-test this should include the t-statistic, degrees of freedom, and p-value indicating whether the difference between before and after arrays is statistically significant.
Example use cases
Pre/post intervention significance check
“I ran an experiment with 6 participants and measured their scores before and after a training session. Before: [72, 68, 75, 80, 65, 70], after: [78, 74, 80, 85, 71, 76]. Run a paired t-test and tell me if the improvement is statistically significant.”
A/B testing matched pairs analysis
“I have conversion rates for the same 8 users under two different UI designs — old design: [0.12, 0.18, 0.09, 0.22, 0.15, 0.11, 0.20, 0.17] and new design: [0.15, 0.21, 0.13, 0.25, 0.18, 0.14, 0.23, 0.19]. Do a paired t-test to see if the new design actually improves conversions.”
Medical treatment effect evaluation
“These are blood pressure readings for 5 patients before and after a medication — before: [145, 138, 152, 160, 142], after: [132, 128, 140, 149, 135]. Compute a paired t-test so I can report whether the treatment had a statistically significant effect.”
Technical details
Endpoint
POSThttps://kihustle.tech/nexus/api/v1/t-test-pairedWhen to prefer
Choose this endpoint when you have paired/matched samples (same subjects measured twice, e.g. before and after an intervention) and need to test whether the mean difference is statistically significant. Prefer over independent t-test endpoints when observations are naturally paired. Useful in scientific experiments, A/B tests with matched users, or any repeated-measures scenario.
Side effects
- Mismatched array lengths (before and after arrays must have equal number of elements)
- Empty or null arrays causing computation failure
- Non-numeric values in input arrays
- Arrays with fewer than 2 elements (insufficient degrees of freedom)
- Generic 'processed'/'success' response may lack granular statistical details like p-value or confidence intervals
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| after | array | |
| before | array |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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