{
  "abstract": "Uncertainty quantification for materials property prediction increasingly reports conditional coverage, coverage within strata of the predicted value, alongside marginal coverage. The worst-bin statistics used for this are downward-biased in finite samples, a known defect that applied materials work does not generally correct for. We replicate a single-dataset conditional coverage study across five regression datasets (n = 437 to 21,263), seven interval methods and 50 random splits each, correcting the worst-bin statistic against its expected value under exact conditional validity. The null is simulated from realized per-bin test counts and referenced to each method's own marginal coverage, separating marginal miscalibration from uneven coverage. On the smallest dataset the correction runs from 0.061 to 0.096, between a third and two thirds of the shortfalls actually reported, against 0.009 to 0.011 on the two largest. Split conformal prediction's failure survives the correction on four of five datasets; two conclusions of the antecedent study do not. Conformalized quantile regression, characterized there as a negative case, fails on one dataset only. Cross-fitted normalized conformal prediction, grouped there with the successful methods, fails on three of five. Group-conditional (Mondrian) conformal prediction stays below our significance criterion on every dataset, yet exceeds the null on three of five under an octile refinement of the variable it calibrates on. A level-matched ablation locates that uniformity in group-conditional calibration rather than in the elevated quantile level, and removes Mondrian's apparent width cost entirely. Conditional coverage evaluations should report the null distribution of the worst-bin statistic used, the method's realized marginal coverage, and the resolution of the evaluation partition relative to any partition the method calibrates on.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.14521",
  "author_total": 1,
  "authors": [
    {
      "name": "Irene Boruah"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5r5nknsdt69bt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.14521",
  "fulltext_url": null,
  "id": "5r5nknsdt69bt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.14521",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.14521",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5r5nknsdt69bt.ris",
  "sources": [
    {
      "first_seen": "2026-11-13T06:21:06Z",
      "native_id": "2611.14521",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.14521"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Null-Corrected Conditional Coverage in Materials Property Prediction: A Five-Dataset Replication",
  "title_text": "Null-Corrected Conditional Coverage in Materials Property Prediction: A Five-Dataset Replication",
  "type": "preprint",
  "updated_at": "2026-11-13T06:21:06Z",
  "url": "https://research.draconitic.com/paper/5r5nknsdt69bt",
  "venue": "arXiv",
  "versions": []
}