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  "path": "/t/significance-versus-hypothesis-testing/28638#post_1",
  "publishedAt": "2026-02-13T21:21:10.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "We make a distinction between Fishers approach and the NP approach (Fix α (e.g., 0.05), control type I error, maximize power subject to that constraint). However after reading the arguments between Fisher and NP, I tend to agree with Fisher - beyond sample size calculations and power, there is no real utility of NP’s approach. All “statistical tests” are indeed Fisherian and NP just gave us power that is irrelevant to observational studies and perhaps may help improve efficiency in experimental studies.\n\nIn Ronald Fisher’s framework, the p-value measures strength of divergence from the hypothesized null, there is no alternative hypothesis formally specified, there is no type II error, there is no explicit loss function and there is no fixed long-run decision rule. The p-value answers: P(data as or more extreme∣H0). Fisher viewed this as a continuous measure of divergence, not a mechanical accept/reject device.\n\nFinally, decision theoretic extensions of the NP framework are used in medicine mainly in clinical decision analysis, health economics, and policy modeling, not in everyday p-value reporting.\n\nSo why do we need any of these ideas in relation to p-values - why not just agree with Fisher?. Any thoughts would be of keen interest.",
  "title": "Significance versus hypothesis testing"
}