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  "path": "/t/significance-versus-hypothesis-testing/28638#post_11",
  "publishedAt": "2026-02-16T04:53:12.000Z",
  "site": "https://discourse.datamethods.org",
  "tags": [
    "[1",
    "1]. Further, post hoc assessments of power cannot be done because they are deeply problematic (e.g., they are irrelevant and typically are biased and have large sampling variation) and thus cannot be calculated [[17",
    "[17, [18",
    "17, [18, [19",
    "18, [19, [20",
    "19, [20]. When a power analysis is required, there is a strong motivation to assume a large enough effect size, that matches a power that one desires to be computed [[21"
  ],
  "textContent": "ChristopherTong:\n\n> “…..abandoning” such calculations altogether, which would be ethically reckless,…..\n\nSample size calculations cannot be done with any measure of certainty because they require knowledge of the true effect in the study, which is always unknown (not only before but also after the study is conducted), and which, if known, would make conducting the study unnecessary 1]. Further, post hoc assessments of power cannot be done because they are deeply problematic (e.g., they are irrelevant and typically are biased and have large sampling variation) and thus cannot be calculated 17, 18, 19, [20]. When a power analysis is required, there is a strong motivation to assume a large enough effect size, that matches a power that one desires to be computed [[21]. Therefore it is best that, instead, we should just include all participants who were available within the time frame of the study.",
  "title": "Significance versus hypothesis testing"
}