# P values

`kaal:entity:p-values`

**Status.** derived

This node is assembled mechanically from the 3 claims that carry the concept tag `p-values`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

3 claims across 2 works, 2016 to 2026.

**2016**

- [2816408-019](https://wulfkaal.github.io/claims/2816408-019) [empirical/evidenced] *(failure mode)* -- Across an array of robustness tests, the requirements introduced by the Dodd-Frank Act create no significant effect on private fund performance, with all reported RD p-values above the 5% level.
  > Using an array of robustness tests validating our RD results, Figures 3-7 suggest that the requirements introduced by the Dodd-Frank Act create no significant effect on private fund performance. The P-values for all RD results in Table 6 are above the 5% level and confirm our finding of no effect.
  Wulf A. Kaal, Barbara Luppi, Sandra Paterlini, Did the Dodd-Frank Act Impact Private Fund Performance  – Evidence from 2010 – 2015 (2016). SSRN: https://ssrn.com/abstract=2816408

**2026**

- [7598518-034](https://wulfkaal.github.io/claims/7598518-034) [empirical/stated] *(failure mode)* -- A substrate was declared procedure-invalid by the operator before any run or reading, with no pre-run rule fired and no written reason recorded, and its nine registered tests stand at a p-value of one with no other label, so the filter is tested on one substrate, a two-dimensional mixture with simulated validators.
  > Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.
  Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518
- [7598518-037](https://wulfkaal.github.io/claims/7598518-037) [design/stated] -- A contrast is superior when its Holm-adjusted p-value is below 0.05, its 95 percent interval lies above zero, and its point estimate is at least 0.10; it is negative when the adjusted p-value is below 0.05 and the interval lies below zero, a substantive null when its 90 percent interval lies within plus or minus that margin, and inconclusive otherwise.
  > Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.
  Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/p-values.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.