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Statistical Tests – Power & Sample Size for t-Tests
x402
Statistical Tests – Power & Sample Size for t-Tests is a paid API for AI agents from stats-tests.x402tookit.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-21).
Calculates the required sample size for a t-test given effect size, alpha, and power — or returns the achieved power for a given sample size.
Price / call$0.02
Success—
StatusUnknown
How this service works
Sample size for a two-sample, one-sample or paired t-test from effect size, alpha and power; or the power for a given size.
Output
Returns the required sample size per group (nPerGroup) and total subjects for the given parameters, or the achieved power if nPerGroup is supplied. Also includes the z-alpha, z-beta values, design label, a human-readable summary, verdict, unrounded sample size, and a disclaimer — with any reasons for degraded results flagged.
Example use cases
Clinical trial participant planning
“I'm designing a clinical trial comparing two treatment groups. If I expect a medium effect (Cohen's d = 0.5), want 80% power, and will use alpha = 0.05, how many patients do I need per group?”
Checking power for a fixed sample size
“I only have 40 participants per group for my two-sample t-test and expect a small effect size of Cohen's d = 0.2 at alpha = 0.05 — what statistical power will I actually achieve?”
Paired study design sample size
“I'm running a before-and-after paired study and expect a large effect (Cohen's d = 0.8). With alpha = 0.05 and desired power of 90%, how many subjects do I need to recruit?”
Technical details
Endpoint
GEThttps://stats-tests.x402tookit.com/power-t-test?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, stateless computation of t-test sample size or power — ideal for study planning, grant proposals, or sanity-checking an existing design without running full simulation software. Prefer it over general statistics libraries when you want a pay-per-call API with no setup and Cohen's d as the primary effect size measure.
Side effects
- Missing effectSize parameter returns an error
- Power value outside 0.5–0.99 range may be rejected or produce edge results
- Very small effect sizes may yield impractically large sample sizes
- Invalid design string (not 'two-sample', 'one-sample', or 'paired') returns error
- Alpha or power values outside valid ranges return validation errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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