{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T17:18:14Z","timestamp":1782321494951,"version":"3.54.5"},"reference-count":53,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T00:00:00Z","timestamp":1705881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The paper makes a case that the current discussions on replicability and the abuse of significance testing have overlooked a more general contributor to the untrustworthiness of published empirical evidence, which is the uninformed and recipe-like implementation of statistical modeling and inference. It is argued that this contributes to the untrustworthiness problem in several different ways, including [a] statistical misspecification, [b] unwarranted evidential interpretations of frequentist inference results, and [c] questionable modeling strategies that rely on curve-fitting. What is more, the alternative proposals to replace or modify frequentist testing, including [i] replacing p-values with observed confidence intervals and effects sizes, and [ii] redefining statistical significance, will not address the untrustworthiness of evidence problem since they are equally vulnerable to [a]\u2013[c]. The paper calls for distinguishing between unduly data-dependant \u2018statistical results\u2019, such as a point estimate, a p-value, and accept\/reject H0, from \u2018evidence for or against inferential claims\u2019. The post-data severity (SEV) evaluation of the accept\/reject H0 results, converts them into evidence for or against germane inferential claims. These claims can be used to address\/elucidate several foundational issues, including (i) statistical vs. substantive significance, (ii) the large n problem, and (iii) the replicability of evidence. Also, the SEV perspective sheds light on the impertinence of the proposed alternatives [i]\u2013[iii], and oppugns [iii] the alleged arbitrariness of framing H0 and H1 which is often exploited to undermine the credibility of frequentist testing.<\/jats:p>","DOI":"10.3390\/e26010095","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T08:28:28Z","timestamp":1705998508000},"page":"95","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["How the Post-Data Severity Converts Testing Results into Evidence for or against Pertinent Inferential Claims"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9229-424X","authenticated-orcid":false,"given":"Aris","family":"Spanos","sequence":"first","affiliation":[{"name":"Department of Economics, Virginia Tech, Blacksburg, VA 24061, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"ref_1","unstructured":"National Academy of Sciences (2016). 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