Talk:Statistical inference

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given, but this is done in such a way as it looks as if the citations back-up the opinions. What is needed are some published summaries of the whole field (or wide subfields) of statistical inference (and its development) that can be used as sources for statements about importance, development, relevance, etc.. The tag was originally placed because of a more blatant synthesis which has been removed & replaced by something presumably better, but overall the problem seems to remain, possibly at a lower level.

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logicians, Kolmogorov was a better authority on probability and logic than looking at a recent "statistical journal"; Being the architects of approximation-theoretic and limiting statistics, Le Cam and Pfanzagl are better authorities on asymptotics than is "looking at a recent statistical journal". You used to cite van der Vaart's textbook, which acknowledges the (obvious) priority and authority of Le Cam and Pfanzagl. (I do not object to your removing the "n < infinity" inequality, etc.)

343: 322: 200: 1175:(3-4) Statisticians Rao, Stigler and Kempthorne and logicians von Wright and Hintikka and philosopher Hacking have argued that Peirce is a central figure in statistics, especially in statistical inference. Unlike Fisher, these sources can use "sufficient" and "necessary" correctly in the fourth editions of their books! Unless Melcombe cites a reliable source contradicting these (reliable) sources, his personal feelings about citing Peirce are irrelevant. 1092:

they are worth saying at all; (3) find out how and when to include citations, in particular as accessible sources that can be sought out to confirm that someone else has made the point or other confirmation of formulae etc. ... it need not be to the first person who made the point; (4) resist the urge to include references by Peirce in every article he edits ... it can't always be necessary to include 5 or 6 of such references.

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inference (as opposed to abductive hypothesis generation)? McPastry and Melcombe have altered the text so that it read that the population's normality is a consequence of the central limit theorem (rather than the normality of the sample mean). This is incompetent statistics, which violates the ISI code of ethics, especially since one or both of you label yourselves statisticians.

191: 1172:(2) Elementary logic suggests the problems with false hypotheses: In many systems, one can prove anything via a false hypothesis. Unless this article presents itself as an article in pure mathematics, then some discussion of premises is important, especially since leading authorities suggest that the use of incredible assumptions distinguishes statistical incompetence. 961:(2) permutation tests are most relevant when the data come from randomized procedures (in which case, they are termed "randomization tests"), citing Lehmann. The relevance of permutation tests to non-randomized data is questionable (Hinkelmann & Kempthorne. Basu's paper on the Fisher randomization test could be cited, along with the discussion). 1909:"Your conclusion doesn't follow from the premise. Plenty of educated, smart, reasonable people are aware of Laplacian principle of indifference (giving probability one-half to a proposition in a state of ignorance), monkeys typing at keyboards, etc. as forms of statistical inference, also --- to say nothing of tarot cards and ouija boards." 458: 1715:

I've edited this to remove inflammatory statements about limiting results being "irrelevant". Limiting results - while certainly not providing inference on their own - do provide the basis for approximations that people use in practice, so they 'are' relevant to the inferential process. Moreover, for

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has the form "for every epsilon, there exists some n such that good things happen"; in statistics, such results often are proved via arguments like "assume that there exists a sequence of consistent score estimators and suppose the conclusion fails --- then a contradiction occurs". Such arguments are

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Melcombe raises many points. (1) In particular, Melcombe exagerates my editing about fiducial inference. Indeed, I provided the Lindley reference (whose citation he improved greatly) and described the limitations of fiducial inference. Indeed, I also provided all the references to serious statistical

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It would be good if KW would do a number of things: (1) learn what Knowledge is supposed to be ... it is specifically not a "statement of best practice" and not a place to impose particular viewpoints, as was done in the deletion of (nearly) all mention of fiducial inference simply because KW doesn't

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I ain't no 'Brother', brother. 'Objective' is unhelpful to the lay reader, it suggests that anything else is unfair, or somehow tainted by vested interests. As someone, somewhere, has to choose what analysis to do - subjectively - then use of the term is unhelpful and confusing. I have formatted some

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As I'm sure you read, I did say "probably". I don't believe there is hard evidence either way on the numbers of researchers or amount of work, or indeed that any hard-and-fast ordering is available or useful. I mean that lots of (educated, smart, reasonable) people are aware of fiducial inference as

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No objection to trimming this down a bit, though I feel it merits some discussion, as it's probably the best-known paradigm outside of Bayes and frequentism, whether or not its "sun has set". I think a description of the general idea - even if only for one-parameter problems - would greatly help the

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Oh? Are you now saying that it is not said to be fallacious? In the end it is just one person's opinion and readers can make up their own mind. Even Zabell said that early version of the Fisher approach were indistiguishable from Neynan's, so it can't be that all versions of a fiducial approach are

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I don't like making mistakes and I checked the reference, which was indeed the article I intended. You are correct that a Bayesian posterior is required, which would exclude some generalized Bayesian procedures needed to characterize admissibile procedures, but otherwise includes much of statistics.

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the discussion in the article says nothing about "good approximation for making decisions"; as currently written it is not a useful analogy. Also, the situation in modeling is complicated by e.g. classical linear models giving valid-if-conservative tests for population parameters, in many real-world

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False models and pi=22/7 is a bizarre comparison. Anyone can see 22/7 is not Pi. It would be an overstatement to ridicule the assumption of classical linear models because they're not true. (n.b. I would rather not include the Box mantra on models and usefulness, it's hackneyed and quoted as support

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Mcpastry first removed the recomendations favoring objective randomization (the leading textbooks and on the professional guidelines of statisticians, in the USA and the world) because of his objection to "objectivity", etc.. Now McPastry has again removed those references, without responding to the

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a predict 0 NB. trivially certain prediction 0 0 0 0 0 0 0 0 0 predict 3 NB. uncertain prediction 1 1 1 1 1 1 0 1 predict 3 NB. more information gives better predictions 1 2 1 1 0 0 0 996 predict 4 NB. plenty of information gives good predictions 0.004 0.004 0.004

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Your conclusion doesn't follow from the premise. Plenty of educated, smart, reasonable people are aware of Laplacian principle of indifference (giving probability one-half to a proposition in a state of ignorance), monkeys typing at keyboards, etc. as forms of statistical inference, also --- to say

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Nobody denies that many asymptotically nifty procedures perform well for moderately sized samples (even without having an approximation result proved): On the contrary, I added the precise statement by Hoffman-Jörgensen about the role of experience and simulations in evaluating methods. (Pfanzagl's

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A. You did remove the statement that limiting results are irrelevant to finite samples, which is nearly a quotation from Kolmogorov and Le Cam (many times in his book); Pfanzagl's recent textbook says the same thing. Being the world's leading authority on probability and one of the world's leading

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Well it would certainly help if you could be bothered to supply a proper reference when you are inserting a citation ... after all, Knowledge policy (which you are so keen on making up as you go along) is that you should be looking at the reference at the time you are editing, so it should be easy.

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So let's discuss these references to Peirce. The text says "Objective randomization allows properly inductive procedures", and then lists 6 publications by Peirce to back-up this statement. Is KW saying that all 6 of these are needed for this purpose? And that the same 6 are needed in all the other

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as previously suggested; (2) remember what this article is about ... its title and subject is "Statistical inference" ... there are separate articles on statistics, statistical models and statistical assumptions where things that are not directly relevant to "statistical inference" can be placed if

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My concern is that readers confuse the meaning of this term in logical deduction with its lay meaning - where relevance means having "significant and demonstrable bearing upon the matter at hand" (Merriam Webster). So, saying that e.g. asymptotic results are 'irrelevant' to finite samples suggests

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Saying non-parametric means "very few" assumptions seems too vague; one could say that assuming a classical linear model is just one assumption. I appreciate that it looks very abstract, but the finite-dimensional restriction is in van der Vaart and an Cox - and probably lots of other places too.

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plays with the data regardless of its origins. If a supposed distribution is nice but otherwise nonparametric, then permutation tests characterize best tests (Lehmann's TSH; c.f. Paul Rosenbaum's book on observational studies). Unfortunately, observational data are not i.i.d. but d.d.d., dependent

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I have suggested 'formally irrelevant' as a compromise term. 'Logically irrelevant' could also work. Regarding Kolmogorov/Le Cam/any other great probabilist or statistician you admire, please contain the biographical information to references. The presence of a citation denotes that the statement

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The acceptable conclusion (keep some mention of ficucial inference) may follow from good premises, not one leading to bad conclusions. For example, discussing "fiducial inference" may alert innocent youth that Fisher committed many logical fallacies and mathematical blunders, and that they should

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It may be nearly a quotation, but it didn't read like that. It read like a bald claim that asymptotic approximations are worthless unless n = infinity, which sounds very like "letting the perfect get in the way of the practical" (to quote Efron) and is unhelpful. Rather than 'nearly quote' fairly

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But it wasn't me who destroyed an article without consulting on the talk page and then demanded that everyone else should do so. I see that your inability to cite without requiring telepathy of the reader and future editors has now infected "fiducial probability" as well as the other articles you

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a deduce 0 NB. certain deduction when the sample is empty 0 0 0 0 0 0 a deduce 25 NB. certain deduction when the sample is the whole population 20 5 0 0 0 0 25 0 0 deduce 10 NB. certain deduction when all pupils are in the same category 10 0 0 0 0 0 a deduce 10 NB. uncertain

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The synthesis tag is dated March 2012, later than 11 June 2010. The synthesis relates (throughout the article) to various opinions being expressed for which there are no sources given (which should be given, otherwise it is OR) in relation to a number of published works, for which citations are

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This statement about fiducial being the 3rd most popular is false. Compare the number of researchers working on information-theory and those working on "fiducial" inference, which was ignored by JASA 2000, etc. It may be true that fans of David Cox and John Nelder (likelihood "wallahs" in Basu's

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about the non-additivity of so-called "fiducial probability". Previously, I did not specify the exact article in JRSS, but described the exponential-family result and suggested a year: I believe that Melcombe specified the currently cited article --- maybe he chose the wrong one, because I don't

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So you have forgotten your edit summary 'remove "fidual inference" with belongs with "flat earth" etc. theories'. It is obvious that any encyclopedia article on statistical inference must say at least a modest amount about it. The fact that there is consensus that it can't be made to work can be

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McPastry and Melcombe have again asserted that statisticians can assume that data arose from simple random sampling --- Where is the quote from David Cox (whom McPastry cites for authority) that allows an iid assumption to be made (without subject matter knowledge or randomization) for inductive

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Regarding (6) Rissannen certainly does discuss "inference" in his little green book. I don't have time to add "inference" now or cite the pages in the articles referenced. I'll try to do it Friday or this weekend. In any event, information-theoretic and Kolmogorov-complexity have shaken up even

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The randomization-based models section should start with something to orient the reader. For example, "An important experimental situation where strong assumptions are well-motivated is the randomized study". Then say how inference proceeds. Then compare randomization-based inference to other

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Thanks for the information on this page. I'm not in favor of merging this page with Inferential statistics or statistical induction. I can see how the statistical inference page compliments the inferential statistics page, but for myself... I needed the information on this page (statistical

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deduction in the general case 8 2 0 1 1 0 a induce 1000 NB. How are the 1000 pupils in the whole school distributed? 751.25 213.929 34.8214 79.516 75.3499 34.0783 a predict 25 NB. How are 25 pupils in another class distributed? 18.75 5.35714 0.892857 2.92691 2.77356 1.25439

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These results are invoked often, agreed. However, a limiting result is irrelevant to the problem at hand, while approximation results and simulations are relevant. I would suggest your reading Philip Wolfe's paper on "A universal algorithm for optimization" (Math. Programming c. 1973) for

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You are correct that some expansions are useful in practice. I would welcome your adding something about Taylor-series expansions of nice functions (or Padé approximations), etc. and their use in practice: See Pfanzagl's LNMS monograph for nice examples, which also warn against fallacies.

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McPastry, I speculated whether Melcombe had "maybe" chosen the wrong article. Knowledge policy on Talk pages requires that substantial comments be retained, even when an editor (like myself here) would be tempted to hide speculation. WP policy also frowns on inserting comments out of time

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The deduce program is the well known formula for mean value and standard deviation of the multivariate hypergeometric distribution. The possibly original research is the observation that the prediction formula follows from the deduction formula by increasing by one and changing sign.

748:" article, which was written after reading about and apparently being able to verbalize some of the fallacies of fiducial probability and inference. You inserted the same nonsense in this article and then defended it, despite (in some sense) knowing better. Again, your tolerance for 815:

IMHO, this editing is improper unless I have mis-represented the ASA and ISI guideliens or the textbooks. If you want, you can cite Robert or Lindley that "objectivity" is misleading, etc., but please label that view as contrary to the professional guidelines and the mainstream

1776:" is notoriously only a collection of (one-parameter) examples --- see Neyman. I would welcome your deleting the whole subsection from this article, because even recent neo-Fisherians (Davison, etc.) think that its sun has set. (The "See also" section could list the article on 1204:

Melcombe's understanding of English puzzles me---especially Melcombe's misuse of "always" and "every" and "must" and "necessary" --- the last word gave Fisher trouble, also, as I noted before (in a remark I gently hid before Melcombe's latest contribution, but which is now

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they have no important place in the analysis of any actual inference based on real data (which must have finite sample size). Such a statement would be far too strong; these results get used all the time, successfully, and demonstrably - look in any applied stats journal.

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I don't recognize any fallacy in what I wrote (which, as you'll recall, was that it seems worthwhile to me that the article mention fiducial inference, briefly). If that isn't your opinion, fine. Why are you trying to make this a bare-knuckle epistemological debate?

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I have repeatedly edited KW's comments, which imply that randomization is the only defensible approach to inference - see comment on "playing with data sets" above. There are several approaches to inference, all of which merit impartial and neutral-toned discussion.

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I'm sorry that you feel insulted. I had wished rather that you should have recognized the fallacy of your argument from its ability to generate such conclusions, and so that you should have been motivated to formulate a viable argument (or withdraw your position).

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despite its being used in citations from Kolmogorov and Le Cam. This is point-of-view editing. I ask that McPastry cite a reliable source explaining why Le Cam and Komogorov are wrong to use "irrelevant", and otherwise stop point-of-view censorship. Thanks.

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McPastry repeatedly has removed the description of randomization procedures for randomized data (data from randomized experiments or random samples) and substituted permutation tests for "data sets". I have asked McPastry repeatedly to stop such editing.

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The distinction between Bayes/frequentist and objective/subjective seem largely unhelpful; see e.g. Christian Robert's book for a strongly argued statement that there's no such thing as objective analysis - ultimately, someone has to choose what to do.

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many applied uses of multivariate regression, in practice the degree of approximation to the true sampling distribution is by far not the biggest problem with inference... calling GMoM or GEE (or their justification) "irrelevant" is not appropriate.

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I rewrote a short version of the distinction between approximation results and limiting results. The footnote now quotes Kolmogorov, Le Cam and Pfanzagl on the irrelevance of limiting results to finite samples, and now gives precise page references.

1305:;) It's certainly doomed as original research, since I don't know of a reference. This example was another example of my striving for a neutral point of view, rather than just signalling skepticism about model-based inference, if I may say so. 1160:

It's a waste of space to discuss the original, since Fisher's "theory" collapses --- an hour's reading should establish that this statement is consensus. It's unfortunate that Melcombe still has failed to correct the damage he inflicted on the

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What is Fisherian statistics? Is that "fiducial statistics" which collapses outside of exponential families (Lindley, JRSS around 1955 or so) or the subjective probability-model "theory" which was ridiculed by Frank Ramsey and Rudolf Carnap?

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These authors state a falsehood. Look at JASA 2000 for e.g. information theoretic statistics, which has featured contributions by Kolmogorov, Turing, etc., and explain into which of these pigeon-holes information-complexity statistics falls,

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KW continues to insert warnings into neutral-toned descriptions of what assumptions are. Validity of assumptions is a good topic, but it can't help the reader (who's reading e.g. a description of three levels of assumption) to distract them

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Like the flat-earth theory, "fiducial inference" made claims that were false. There are many such failed systems of "inference", which are not mentioned. Where is the discussion of "fiducial inference" (or "flat-earth theories") in

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There are several approaches to arithmetic, including statements like 1+1=3. However, it is hard to find reliable sources supporting 1+1=3, and easy to find reliable sources supporting alternatives, especially the alternative that

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Both Neyman and Fisher agreed that their conceptions were different and both viewed the other's as wrong-headed. Your edits now suggest substantial agreement (for early versions of Fisher's "theory", which even he discarded!).

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Using 3 and 22/7 seem good examples, that show a false-hood can be a good approximation and useful for making decisions, although it can be improved with the scientific method, leading to the truth as the limit of so reasoned

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books have lots of simulation studies, as well as very precise asymptotics.) The objection is to the illegitate inference from limiting results to some fixed sample, without referring to simulation studies, experience, etc.

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Randomization-based models: this currently doesn't start with a description of what randomization-based inference is. Commentary along the lines of "a good observational study may be better..." seems unnecessary, to

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Fiducial Inference: this never says what fiducial inference actually is. I don't see why a reader would care that Fisher couldn't settle on a definition of something that hasn't been even vaguely described in this

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Randomization-based models: discussion of whether or not there is a true model seems of marginal interest (there's not, yet the world doesn't end because of it). Pi and 22/7 seems an unhelpful analogy. Also, more

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Please read my entry above. You are interpreting 'irrelevant' in its strict mathematical sense (and I'm not disagreeing with what you say). But the quotation of Le Cam in the article states that limit results

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a form of statistical inference, and as such it merits some discussion in this encyclopaedia entry. By stating that my statement was "false" you, again, appear to lack a sense of diplomacy, and of proportion.

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hold for some definite (finite) sample size (described in terms of the parameters), and often for all n. (C.f. Kantorovich's "Functional Analysis and Applied Mathematics" where this distinction is stressed.)

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Much of the rest of this article (e.g. all the randomization stuff) heavily accentuates discussion/criticism of methods, rather than elucidation of what the methods actually are, and how they are justified.

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Fiducial/Lindley - Lindley (1958) didn't show fiducial inference "fails", he showed that it didn't correspond to a Bayesian procedure - this isn't quite the same. Don Fraser has written about this (well).

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21:49, 6 Nov 2003 (UTC)) Actually thats not true. In the case of ozone, the obs and theory are suffiently good that statistics aren't needed. GW has theory, modelling and obs too, but stats are important

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Why is that an objection to the proposed merger? No one's proposing getting rid of this information---just incorporating some of it into that other article, and maybe putting some of it somewhere else.

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Richard Berk's book is a good accessible reference for bogosity of models. It does, however, largely assume that one really wants to know about a true underlying finite-dimensional parametric model.

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uses the randomization described in the experimental protocol and implemented, and is not assumed. Similarly, in sampling, the random sampling scheme defines a distribution of the sample statistic.

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statement of Robert cannot be viewed as mainstream statistics. It contradicts the ISI Code of Ethics, and the curriculular guidelines of the ASA, at least, and the statements of leading textbooks.

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Dear Brother McPastry, I am delighted to find another statistician that wants to reduce the schismatics between Bayesian, not necessarily Bayesian, or anti-Bayesian statistics, as presented on WP.

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McPastry has deleted referenced statements to the contrary, made by leading statisticians, with precise citations, and has failed to strive for consensus. This editing violates Knowledge policy.

1917:"I mean that lots of (educated, smart, reasonable) people are aware of fiducial inference as a form of statistical inference, and as such it merits some discussion in this encyclopaedia entry." 1489:

I did look at Knowledge articles on scientific theories, often by scientists with path-breaking work in other areas, that are widely dismissed by other scientists. The term for theories like

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Simple random sampling is an assumption that can be made in fully- semi- and non-parametric models. So it's perhaps not best to state it as the sole example in the non-parametric bullet.

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I agree with comments 1-4: However, regarding (1), it is a bad idea to introduce nonparametrics with "infinite dimensions" in Knowledge, because you will scare most of the audience.

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Please merge the pages 'statistical inference' and 'Inferential statistics' , but be aware that the page on statistical inference has more concice information than the other page.

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Information and computational complexity: looks okay, but the comparison to e.g. Bayes is redundant - it's mentioned elsewhere in the article, as are its strengths and weaknesses

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I hadn't noticed this, what obviously must be a joke, before. I say "joke" because of the unilateral way that KW destroyed the pre-existing article without seeking consensus.

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It's an encyclopedia; entries are there by judgement, not by logical deduction. Also, I find your allusions to monkeys, tarot and ouija insulting, and way out of proportion.

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Melcombe, can you find a reliable source that states that Fisher's argument in 1955 wasn't fallacious? The fallacy was dissected by Neyman in JRSS 1956. Does the article on

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2000? That review of statistics had discussions on information complexity, which Melcombe neglected to discuss while he inserted "fiducial inference" in yet another article.

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wrote up fiducial inference for this article suggests I am not alone. Accusing me of falsehoods, fallacies, and making other insulting comments helps no one. Please stop.

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It might be more useful to discuss distinction between non- semi- and fully-parametric inference, the distinction of which is orthogonal to the Bayes/frequentist choice.

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I was criticizing your commenting that my statement was false. Why do you persist in arguing over this? We agreed that the fiducial inference section could be trimmed.

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after I had provided precise citations with page numbers and the relevant quotations. On the question of "irrelevance" your editing has been very point-of-view, imho.

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and differently distributed (Freedman, Statistical models), so the relevance of permutation tests on observational data depends on implausible assumptions.

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I thank the other editors for recent improvements, which try to preserve the truth of others' (particularly my) statements (while trying to correct errors).

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advanced texts, it would be better to explain what the authors mean, in the simplest terms possible... all without belittling other approaches, of course.

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Source: Essentials of Statistical Inference (Cambridge Series in Statistical and Probabilistic Mathematics) by G. A. Young (Author), R. L. Smith (Author)

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Why are you so fussed about the statement of an opinion? "I feel it merits some discussion" was where we started. I still feel that way. The fact that

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No statement written by me implies that "randomizaton is the only defensible approach". Please quote one offending text, McPastry or Melcombe (or both)!

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a 20 5 0 -(1+a) _21 _6 _1 (-(1+a))deduce 25 NB. this is how prediction works. Amazing! 18.75 5.35714 0.892857 2.92691 2.77356 1.25439

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Fisher's last descriptions of fiducial inference involved conceptual confusions and obvious fallacies, in fact, not just in Neyman's reading.

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I can respond to only the most important comments, which I have numbered for convenient reference. Your tone & comments are constructive.

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of the rest of the text to incorporate different types of models, and different modes of inference - all combinations of which are possible.

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Thanks for making this article. I've been wanting it for a long time. It has a bearing on environmental issues, because the case for a

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when it was cited with references to Kolmogorov's article and LeCam's book (but not quotations or page number): In fact, your editing

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article's lead sentence, even though he's apparently read about the failings of Fisher's fiducial system. (I have less tolerance for

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Hope you are similarly minded. I intend to continue to remove pejorative language, which is inappropriate in an encyclopedia entry.

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reference in this article, which wasn't so clearly exciting after all---thanks for the discussion. (I never introduced the Lindley

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circumstances. Using pi=3 or 22/7 doesn't capture this, there isn't a population-parameter interpretation to the area of a circle.

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The scare-quotes on "statistician" were in the cited text. See Hinkelmann and Kempthorne, Exercise 6.3 (page 193 in first edition).

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The new intro is a nice improvement. I agree with Melcombe about scope (and much else). Some issues I think could be cleared up

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So now KW, tell us exactly where you think Cox is being cited as a justification for advice to take any particular approach.

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The first line of the following answers contains the orders of magnitude, and the second line contains the uncertainties.

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English "statistical inference", whose obsessions with Neyman and Fisher have long recognized as unhealthy, eg. by Cox.

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Editor McPastry has labeled the word "irrelevant" as inflamatory and inappropriate to this article, and has repeatedly

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inference) alone and do not at this time need the depth of information on the other page (inferential statistics).

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article, which may be of interest to Bayesian or fiducial fellow-travelers, because it was cited by Cox's recent

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If you didn't previously specify a title, perhaps saying that Melcombe "chose the wrong one" lacks diplomacy?

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mentioned, but is itself irrelevant to the question of whether it should appear. Recall what Knowledge is.

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Information and computational complexity - doesn't mention inference anywhere, so not obviously relevant

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Rather than claim that randomization is the only defensible approach, I have consistently written that

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clarification about the distinction between limiting results and finite computations/observations.

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wildly nonconstructive and irrelevant to finite samples. Kolmogorov, Le Cam and Pfanzagl are right.

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It may be useful to consult a dictionary on "foolhardy" (which is gentler than a reliable source,

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on Knowledge. If you would like to participate, please visit the project page, where you can join

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on Knowledge. If you would like to participate, please visit the project page, where you can join

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on Knowledge. If you would like to participate, please visit the project page, where you can join

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made has support; the flow of the text's description about e.g. what limiting results provide is

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There are actually three schools of statistical inference: Bayesian, Fisherian and frequentist

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a =: 20 5 0 NB. There are 20 pupils in category A, 5 in category B, and 0 in category C.

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OK. The resulting text seems much improved over either of our earliest versions. Thanks.

1927:'s argument for the benefits of reading fallacious arguments and wrong positions.) Thanks 1368:

cites the appropriate Lindley paper. Again, I'll try check in the next few days. Thanks!

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Was that the tagged synthesis? If not it should be pointed out, if so the flag removed.

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Example. Based on a test the pupils in a school class are classified into categories.

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McPastry, please seek consensus on the TALK page before doing such unilateral editing.

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dismissed the sentence with the comment "Look at any statistical journal to see why"

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I would consider if a courtesy if you would restore the mainstream recommendations

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disrupted by the insertion of reasons why these specific authors are being cited.

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McPastry has failed to cite one reliable source opposing propositions (1) and (2).

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this example is simpler than Peirce's example of iterative methods (e.g. Newton).

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think twice before partaking in the initiation rites of the Fisher cult. (C.f.

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Okay, the last round of editing retained the word "irrelevant". I dislike the

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reference in the "fiducial inference" article.) However, I leave the Lindley

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deduce =: %~`*/"2@(,:(%:@*-.))@(+/@[%~1,,:) predict=: (deduce~-@: -->

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See above for why. I dislike your description of my text as weasel words.

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work that had been inspired by Fisher's writings on "fiducial inference".

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Many or most of your recent edits are improvements imho. However, ...

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Barnard's work has inspired further work by D. A. Fraser, P. Dawid,

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Some things I intend to work on (and would appreciate comments on);

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Perhaps a little more in the way of examples would help the reader?

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This is pretty hilarious, Melcombe, given your destruction of the "

992:, "damned fool", whose exact citation I couldn't find via Google.) 698:

points---especially regarding the need to cite reliable sources.

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relevant in the lay sense. Hence, disambiguation is required.

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Regarding Lindley (7), tonight I cited Lindley's interview in

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The following may be original research, so (according to

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terms) are arguably more popular: Is that what you mean?

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state that "this theory is sometimes said to be false"?!

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On the contrary, I clarified the "nonparametric" use of

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If the uncertainty is zero then the result is certain.

2186:. Box used "locally" objective priors; Jaynes proposed 1988: 1739: 1735: 1731: 160: 1914:

was intended to clarify the error in your statement:

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look forward to some inferential content here, then

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Many of these approaches are related to 1251:"warrants" is nowhere near the right word. 421: 316: 227: 1087:like it's being mentioned ... start from 2327:is estimating from one part to another. 1740:removed the statement about "irrelvance" 2402:Knowledge vital articles in Mathematics 903:Neutral tone versus pejorative language 423: 318: 229: 188: 1732:removed my statement about irrelevance 1255:by people with very different stances) 2417:C-Class vital articles in Mathematics 2321:is estimating the whole from a part. 2315:is estimating a part from the whole. 7: 923:Agreed. Basic considerations are at 469:This article is within the scope of 364:This article is within the scope of 259:This article is within the scope of 2427:High-importance Statistics articles 218:It is of interest to the following 23:for discussing improvements to the 2452:Low-importance psychology articles 1730:I ask that you respond below. You 979:Regarding "perjorative" phrasing: 14: 2442:Mid-priority mathematics articles 2197:, in particular to the theory of 547:is entirely based on statistics. 384:Knowledge:WikiProject Mathematics 2397:Knowledge level-5 vital articles 1595:limitations of randomized trials 1522:ontogeny recapitulates phylogeny 489:Knowledge:WikiProject Psychology 456: 446: 425: 387:Template:WikiProject Mathematics 351: 341: 320: 279:Knowledge:WikiProject Statistics 252: 231: 198: 189: 45:Click here to start a new topic. 2457:WikiProject Psychology articles 2432:WikiProject Statistics articles 509:This article has been rated as 492:Template:WikiProject Psychology 404:This article has been rated as 299:This article has been rated as 282:Template:WikiProject Statistics 2407:C-Class level-5 vital articles 1983:" of limits for finite samples 1: 2377:10:41, 18 December 2012 (UTC) 2164:group-transformation families 1989:removed the word "irrelevant" 833:21:42, 28 February 2010 (UTC) 689:21:21, 28 February 2010 (UTC) 665:21:06, 28 February 2010 (UTC) 630:17:25, 25 February 2010 (UTC) 483:and see a list of open tasks. 378:and see a list of open tasks. 273:and see a list of open tasks. 42:Put new text under old text. 2437:C-Class mathematics articles 2447:C-Class psychology articles 2422:C-Class Statistics articles 1645:On the distinction between 576:22:55, 4 October 2006 (UTC) 50:New to Knowledge? Welcome! 2473: 2132:19:21, 27 March 2010 (UTC) 2110:19:08, 27 March 2010 (UTC) 2080:19:21, 27 March 2010 (UTC) 2055:20:35, 27 March 2010 (UTC) 2041:20:00, 27 March 2010 (UTC) 2022:19:28, 27 March 2010 (UTC) 2002:16:49, 27 March 2010 (UTC) 1955:23:38, 27 March 2010 (UTC) 1937:21:25, 27 March 2010 (UTC) 1902:20:49, 27 March 2010 (UTC) 1887:19:02, 27 March 2010 (UTC) 1872:03:31, 25 March 2010 (UTC) 1858:16:00, 21 March 2010 (UTC) 1841:03:31, 25 March 2010 (UTC) 1824:04:16, 19 March 2010 (UTC) 1809:23:16, 17 March 2010 (UTC) 1794:06:17, 17 March 2010 (UTC) 1756:16:57, 27 March 2010 (UTC) 1726:03:49, 25 March 2010 (UTC) 1711:22:27, 21 March 2010 (UTC) 1684:23:11, 17 March 2010 (UTC) 1641:06:17, 17 March 2010 (UTC) 1618:07:12, 16 March 2010 (UTC) 1572:16:35, 12 March 2010 (UTC) 1550:16:20, 12 March 2010 (UTC) 1534:15:40, 12 March 2010 (UTC) 515:project's importance scale 2282:An unknown number may be 2219:21:07, 11 June 2010 (UTC) 2154:and Steffan Lauritsen on 2141: 1511:23:40, 4 March 2010 (UTC) 1467:12:51, 5 March 2010 (UTC) 1452:08:16, 5 March 2010 (UTC) 1437:22:21, 4 March 2010 (UTC) 1407:22:10, 4 March 2010 (UTC) 1392:01:06, 5 March 2010 (UTC) 1378:21:56, 4 March 2010 (UTC) 1353:01:06, 5 March 2010 (UTC) 1333:01:06, 5 March 2010 (UTC) 1319:01:06, 5 March 2010 (UTC) 1294:01:06, 5 March 2010 (UTC) 1272:21:35, 4 March 2010 (UTC) 1223:17:47, 4 March 2010 (UTC) 1200:17:39, 4 March 2010 (UTC) 1185:17:10, 4 March 2010 (UTC) 1144:12:46, 5 March 2010 (UTC) 1105:16:39, 4 March 2010 (UTC) 1075:14:42, 4 March 2010 (UTC) 1056:00:44, 4 March 2010 (UTC) 1040:at the first bullet point 1028:14:50, 3 March 2010 (UTC) 1006:11:53, 3 March 2010 (UTC) 937:10:05, 3 March 2010 (UTC) 918:19:50, 2 March 2010 (UTC) 898:09:14, 2 March 2010 (UTC) 848:01:21, 1 March 2010 (UTC) 797:12:38, 5 March 2010 (UTC) 762:09:57, 5 March 2010 (UTC) 724:09:36, 5 March 2010 (UTC) 508: 441: 403: 336: 298: 247: 226: 80:Be welcoming to newcomers 2296:probability distribution 2253:19:59, 24 May 2012 (UTC) 2234:19:13, 24 May 2012 (UTC) 874:nonparametric statistics 607:20:26, 31 May 2008 (UTC) 410:project's priority scale 1516:"Said to be fallacious" 367:WikiProject Mathematics 2392:C-Class vital articles 2306:of the distribution. 2170:. 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Connolley 390:mathematics articles 105:No original research 2334:expressions apply. 2191:prior distributions 2142:Barnard's influence 1361:Statistical Science 821:in the introduction 495:psychology articles 285:Statistics articles 2304:standard deviation 2288:order of magnitude 2176:Bayesian inference 2166:and one-parameter 2160:statistical models 1778:Fiducial inference 1774:Fiducial inference 1499:fiducial inference 1480:Fiducial inference 1411:I hid the Lindley 1163:Fiducial inference 746:fiducial inference 677:"anti-objectivity" 359:Mathematics portal 214:content assessment 86:dispute resolution 47: 2195:harmonic analysis 950:permutation tests 668: 651:comment added by 609: 597:comment added by 529: 528: 525: 524: 521: 520: 464:Psychology portal 420: 419: 416: 415: 315: 314: 311: 310: 183: 182: 66:Assume good faith 43: 2464: 2211:Kiefer.Wolfowitz 2180:George E. P. 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