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KiHustle Paired T-Test
x402
KiHustle Paired T-Test is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-13).
Performs a paired (dependent samples) t-test on two numeric arrays to determine statistical significance of the difference between before and after measurements
Price / call$0.002
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object indicating whether the paired t-test was processed successfully, with a 'result' field confirming processing and a 'status' field showing success or failure. Detailed t-statistic, p-value, and significance findings are expected in the result payload.
Example use cases
Pre/post intervention significance test
“I ran a weight-loss study with 8 participants and measured their weight before and after the program. Before: [85.2, 92.1, 78.4, 88.6, 95.3, 80.1, 87.7, 91.0] and after: [82.1, 89.3, 76.8, 85.2, 91.4, 78.5, 84.9, 88.2]. Can you run a paired t-test to tell me if the weight loss is statistically significant?”
A/B test on matched user pairs
“I have conversion scores for the same users under two different UI designs — old design: [0.32, 0.45, 0.28, 0.51, 0.39] and new design: [0.41, 0.50, 0.35, 0.58, 0.44]. Please do a paired t-test to see if the new design performs significantly better.”
Training performance improvement check
“My team tracked employee productivity scores before and after a training session: before [72, 68, 75, 80, 65, 78] and after [79, 74, 81, 85, 70, 83]. Can you run a paired t-test to determine whether the training made a statistically significant difference?”
Technical details
Endpoint
POSThttps://kihustle.tech/bot-bazaar/api/v1/t-test-paired?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have paired or repeated-measures data (the same subjects measured before and after an intervention or under two conditions) and need to test whether the mean difference is statistically significant. It is specifically suited for dependent samples rather than independent groups.
Side effects
- Arrays of unequal length — paired t-test requires matched pairs so mismatched array sizes will cause an error
- Empty arrays provided for before or after
- Non-numeric values in the input arrays
- Single-element arrays insufficient for variance calculation
- Network timeout or payment authorization failure
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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