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Money Mind Multiple Testing Correction
x402
Money Mind Multiple Testing Correction is a paid API for AI agents from money-mind-extract.onrender.com, paid per call via x402, $0.2/call, status unknown (last checked 2026-10-18).
Applies family-wise multiple testing correction to a backtest winner's t-statistic to determine if it survives statistical scrutiny after searching many strategies.
Price / call$0.2
Success—
StatusUnknown
How this service works
Pay-per-call adjudication for trading research. Eleven gates that killed 35 of our own concepts. Is your backtest real, is your price data real, is your t-stat inflated? USDC on Base, no account needed.
Output
Returns a JSON object with a plain-English verdict (e.g. 'DOES NOT SURVIVE'), the minimum t-statistic required for significance at the 5% family-wise level given the number of tests run, and the expected best t-statistic you'd see from pure noise across that many trials.
Example use cases
Quant researcher validating backtest winner
“I ran 80 different strategy variants and kept the one with a t-stat of 3.4 — can you correct for the fact that I searched 80 of them and tell me whether the t-stat is still significant or just noise?”
Algo trader checking parameter sweep
“I grid-searched 200 parameter combinations on my mean-reversion strategy and the winner has a t-statistic of 3.9. Does that actually survive multiple testing correction, or is it just the luckiest draw from 200 tries?”
DeFi strategy developer screening results
“My team screened 30 different DeFi yield strategies and picked the one with t-stat 2.6. Before we allocate capital, tell me if that t-stat is real or inflated by the selection process.”
Technical details
Endpoint
POSThttps://money-mind-extract.onrender.com/multipletest?utm_source=zero.xyzWhen to prefer
Use this endpoint when you have already identified a best-performing strategy or parameter set from a search across multiple candidates and need to know whether its t-statistic is genuinely significant or inflated by selection bias. Prefer this over generic p-value calculators because it specifically accounts for family-wise error rate from multiple comparisons, returning the corrected threshold and expected noise baseline rather than just a raw p-value.
Side effects
- Missing required fields (type, method, body) returns validation error
- Number of strategies tested set to 1 makes correction trivial and may return unexpected results
- Extremely large numbers of strategies tested may produce very high required-t thresholds that seem implausible
- Payment not provided or insufficient USDC triggers 402 payment required
- Malformed body schema returns HTTP 400
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"verdict": "DOES NOT SURVIVE",
"required_t_for_5pct_family_wise": 4.05,
"expected_best_t_from_noise_alone": 3.116
}
}Tags
x402
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