{
  "abstract": "Policy decisions often hinge on conventional p-value thresholds, which ignore economic costs and benefits of further data collection. This paper recasts statistical significance as a choice between making an immediate policy recommendation and deferring it until further evidence is collected. The welfare-optimal decision corresponds, under minimax regret, to a statistical test whose level depends on the cost and precision of additional evidence. Inverting this rule, we introduce and recommend reporting the abstention-value (A-value) to determine where additional data collection is most needed. The A-value defines the break-even welfare cost of abstaining and recommending further experimentation given the initial evidence. Computing the A-value only requires a point estimate and its standard error, imposes no prior assumptions on policy effects, and can be converted to a monetary research budget using inputs already frequently used by regulators and funding agencies. When experimentation capacity is limited, we show that prioritizing additional data collection where A-values are the largest yields strong finite-sample guarantees. Applications to anti-poverty programs illustrate the practical benefits of using A-values.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.09544",
  "author_total": 2,
  "authors": [
    {
      "name": "Aristotelis Epanomeritakis"
    },
    {
      "name": "Davide Viviano"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fyzzvd15px9ba.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.09544",
  "fulltext_url": null,
  "id": "fyzzvd15px9ba",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.09544",
  "language": null,
  "licence": "CC0-1.0",
  "licence_label": "CC0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/publicdomain/zero/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "econ.EM"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7212178375",
  "pdf_url": "https://arxiv.org/pdf/2609.09544",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fyzzvd15px9ba.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:33:54Z",
      "native_id": "2609.09544",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.09544"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "When is statistical evidence strong enough? Using hypothesis tests to value data collection",
  "title_text": "When is statistical evidence strong enough? Using hypothesis tests to value data collection",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:54Z",
  "url": "https://research.draconitic.com/paper/fyzzvd15px9ba",
  "venue": "arXiv",
  "versions": []
}