{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreifi5h3irjpjf5kheyn2ezj4ayg7mumjyoehxdtcvk4qarbfjfyryy",
    "uri": "at://did:plc:wwyqal4cnqhuwyacdj7rqq3n/app.bsky.feed.post/3mftcjtbkgaf2"
  },
  "path": "/t/change-the-range-not-the-language-on-confidence-intervals/27740?page=2#post_27",
  "publishedAt": "2026-02-26T16:18:29.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "karlamoPA:\n\n> For the broader public, it can be even more concise I feel, such as: the wide range in the CI indicates that additional study is needed to gain confidence that the results in this study predicts the possibility of benefit / risk of harm in other patients.\n\nNot exactly because we have to distinguish two things - the long run interpretation and the “realized interval” interpretation. When Neyman proposed this in 1937 or thereabouts he focused on the interpretation of the 95% interval as a coverage probability i.e. when the same type of study is replicated with sampling from the same population in the same exact way, 95% of such future intervals will include the population parameter. This is the long run interpretation and only holds before your study. Once the study is conducted and your interval created, this is now a single “realized interval” and this definition does not hold. Until recently, no one could interpret this except that the probability of inclusion of the population parameter was 0% (no) or 100% (yes) and 95% did not apply anymore. We proposed that just consider the 95% interval to be the range of test hypotheses (remember that each test hypothesis is a sampling probability model) under which your study data or more extreme has at least a 1-0.95 probability under any of these test hypotheses. The interpretation is that the interval is a range of sampling probability model means (keeping in mind that the mean of a sampling probability distribution is the population parameter) that are supported by your data at the threshold that you choose (e.g. 1-0.95). The 95% is therefore what central percentage of the distribution you choose as the definition of “not unusual” for your data.\n\nNow once the above is understood, all is clear. This is why Fisher and NP had a great disagreement because Fisher realized that the interval was an inversion of his “test of significance” but had nothing to do with the NP “test of hypothesis“. So they could not simultaneously create an interval of this type yet claim that the “test of hypothesis” was an improvement over Fisher’s test of significance. After reflecting on their disagreement, I think Fisher failed to focus on the “realized“ interval and claim it for himself because he was too busy rejecting the concept of the test of hypothesis.",
  "title": "Change the range not the language on confidence intervals"
}