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  "path": "/t/significance-versus-hypothesis-testing/28638#post_3",
  "publishedAt": "2026-02-14T16:07:14.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "I would agree completely with you on these interpretative issues and the broader issue of utility. My point is that we nevertheless have these questions pop up as _p_ values and CIs are not going away any time soon. My question is that if I am asked what is the utility of type I and II errors and alternate hypotheses over and above whatever we get from Fisherian _p_ values, then the answer is _**nothing**_. I agree that _p_ values deal very poorly with questions of real interest to us, but if someone asks for an explanation of these concepts I plan from now on to say that what NP proposed has not even got that nominal inferential value because its purpose is not inference (at least from my viewpoint). And the final question regarding the NP approach - why would I want to know the probability that I would reject the null hypothesis at this sample size? It is not relevant since it just amplifies the misconception that rejection of a tested hypothesis implies something meaningful, which it does not.",
  "title": "Significance versus hypothesis testing"
}