Talk:Likelihood principle

message: Hello everyone.  Well, two people have reverted my edits about accepting an hypothesis.  The LP implies one can accept an hypothesis on the grounds that it cannot readily be improved.  Edwards uses the two units of likelihood criterion for this, and surely his opinion should carry some weight.  Perhaps this observation should appear somewhere else in the article.  Comments?  Robinh 08:10, 12 December 2006 (UTC)
thread_title: Accept a hypothesis

message: I have trouble seeing the point in the "Voltmeter Story."  If the voltmeter goes up to 100, and all measurements were strictly less than that, in what sense is the population 'censored?' If it is indeed 'censored' wouldn't it also be 'censored' if the voltmeter goes up to 1000 volts?  —Preceding unsigned comment added by 170.223.221.198 (talk) 19:15, 16 October 2008 (UTC)
replies: text: Practically, we can consider 1000 volts as infinity, hence uncensored. Quoting from the section "Likelihood principle reference" below, the story is about whether or or not "having observed a specific value of x, other possible observations do not come into play". Arie ten Cate (talk) 12:13, 31 May 2009 (UTC)

text: Today, browsing through my contributions, I read the voltmeter story. I did not understand a particular phrase. Then I found that this was the revised phrase I discussed above, three years ago. I decided it is time to remove the entire paragraph. I think I am entitled to do so, since it is controversial (between me and the anonymous author of the revision) and is not essential and I wrote the paragraph.  — Preceding unsigned comment added by Arie ten Cate (talk • contribs) 14:41, 22 October 2017 (UTC)
thread_title:  Voltmeter story 

message: The article claims:
:In fact Birnbaum's alleged proof of the likelihood principle, however celebrated, has been shown to be invalid by Deborah G. Mayo in Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science (Mayo and Spanos Eds, Cambridge, 2010,) chapter 7, pp. 305-314
I have asked an expert on the likelihood principle about this article. He responded that the article is nonsense, appears only in a book edited by the author of the article, and that there is apparently no peer-reviewed article by the author making that claim.
I will therefore remove the comment as not being properly sourced. Bill Jefferys (talk) 18:29, 3 September 2010 (UTC)
thread_title:  Mayo article 

message: I just removed this section since it is patently wrong and have offered not sources, reliable or otherwise, since 2011 and the subject is tangentially related to the main article.
This section said thing like
:"Furthermore, as mentioned above, frequentist analysis is open to unscrupulous manipulation if the experimenter is allowed to choose the stopping point, whereas Bayesian methods are immune to such manipulation."
Which is absolutely false since any statistical procedure, Bayesian or otherwise, may suffer from Confirmation bias.Viraltux (talk) 09:54, 27 July 2013 (UTC)
thread_title:  Optional stopping in clinical trials 

message: Dr. Johansen has reviewed this Wikipedia page, and provided us with the following comments to improve its quality:
We hope Wikipedians on this talk page can take advantage of these comments and improve the quality of the article accordingly.
We believe Dr. Johansen has expertise on the topic of this article, since he has published relevant scholarly research:
*Reference : Sren Johansen, 2014. "Times Series: Cointegration," CREATES Research Papers 2014-38, School of Economics and Management, University of Aarhus.
ExpertIdeasBot (talk) 16:24, 11 July 2016 (UTC)
thread_title:  Dr. Johansen's comment on this article 

message: Y=12 seems to provide more information about the experimental outcome than X=3. Y=12 tells you that there was a success on the 12th trial. The fact that X=3 tells you only that there were 3 successes in the first twelve trials; not whether the last trial was a success or failure. So the data in the two cases is *not* the same. Clarification?  — Preceding unsigned comment added by 82.13.14.165 (talk) 19:01, 17 February 2019 (UTC)
thread_title:  Problem in the example 

message: "the use of the principle in applications goes back to the works of R.A. Fisher in the 1920s."
Wow, this is so misleading!. Fisher would have been diametrically opposed to the LP. After all, his early concerns with ancillary statistics and, later, with relevant subsets shows a long history of interest in finding the appropriate reference set. These sets included more than just the observed sample. 27.32.95.10 (talk) 19:34, 1 January 2022 (UTC)
thread_title:  Fisher and the LP 

message: I know that this is not a particularly entry-level statistics concept, but can anyone help to make the introduction less technical? I'm admittedly not the best when it comes to statistics so I won't try but I feel like this is completely inaccessible to me and I have a full year of uni stats under my belt. Adamopoulos (talk) 18:51, 18 December 2022 (UTC)
thread_title:  Readability of introduction 

message: The current Introduction contains two descriptions of the likelihood principle.
1.  "In statistics, the likelihood principle is the proposition that, given a statistical model, all the evidence in a sample relevant to model parameters is contained in the likelihood function."
2. "The likelihood principle is this: All information from the data that is relevant to inferences about the value of the model parameters is in the equivalence class to which the likelihood function belongs."
As they stand, neither description makes the structure of the experiment/statistical model irrelevant because both descriptions only restrict the relevance of information that is “in a sample” or “from the data” to that contained in the likelihood function. The structure of the experiment/statistical model is not part of the sample or the data, so both descriptions miss the essential element of the likelihood principle. GrayBorn (talk) 23:11, 12 March 2023 (UTC)
thread_title:  Likelihood Principle Descriptions Both Wrong 

message: Both versions of the coin flipping example are missing a clear problem statement. The simple version poses the question 'Will you now make some different inference'? Inference about what? Also in the longer version it is not clear what the scientists are exactly trying to achieve. The example starts out with 'Suppose a number of scientists are assessing the probability of a certain outcome'. But that is too vague. Next they all do slightly different things, with slightly different intentions. And then the example concludes by raises the suspicion of controversies about some inference methods. I'm sure that at least in this article any controversies or discrepancy can be settled by just being a more precise.
Does the range of the voltmeter includes 100V as a valid reading? In that case there was no problem with measurements of the engineer. If 99V was it's maximum reading the measurement is indeed invalid. From the story its not clear what happened. Next, wat does it mean when the engineer says that he would have used the second meter in case any voltage had been over 100? How does he know any voltage is over 100V? For that he needs the second meter. But he knows its broken? I don't see what point this story is trying to make.  — Preceding unsigned comment added by 84.104.196.156 (talk) 19:42, 23 May 2023 (UTC)
thread_title:  The examples in this article are hopelessly confusing