Talk:Type I and type II errors

message: The best and most helpful analogy for I/II errors I've ever heard is the courtroom analogy, where a type I error is represented by a wrongfully convicted innocent, and a type II error by a wrongfully acquitted criminal.    — Preceding unsigned comment added by Cpt5mann (talk • contribs) 22:30, 21 May 2020 (UTC)
thread_title: Regarding the simplification of the article

message: The reason that I have suggested the merge is that each of these three pieces desperately need to be understood within the context of the other two.
I have done what I can to make the "matrix" which the combined three will inhabit as clear as I possibly can.
The further work, to some extent, needs someone with a higher level of statistical understanding than I possess. However, I am certain that, now I have supplied all of the "historical" information, supported by the appropriate references, that the statistical descriptions and merging will be a far easier task.
Also, I am certain that, once the articles are merged, and once all of the statistical discussions have become symmetrical in form and content, that the average reader will find the Wiki far more helpful than it is at present. My best to you allLindsay658 22:01, 23 June 2006 (UTC)
thread_title: Merge False negative, false positive, and type III error into this article

message: This article was proposed for deletion on the basis of qualms about its name. I agree that it should be moved, and suggest Type I and Type II errors, but I see no reason to delete. Septentrionalis 21:32, 2 July 2006 (UTC)
replies: text: Very well.  The  was a mistake, as there is material here that should be retained in an Errors (statistical) or Type I and Type II errors article. Conversation with the primary author (on my talk page User talk:Arthur Rubin#Four types of error) suggests he/she intends to improve it. — Arthur Rubin |  (talk) 12:17, 3 July 2006 (UTC)
replies: text: Lindsay658's plans for that improvement seem reasonable. Lindsay, if you could use some assistance, especially in straightening out double redirects, let me know. Septentrionalis 12:56, 3 July 2006 (UTC)
thread_title: Prod?

message: Heeding the advice and guidance of Arthur Rubin and Septentrionalis this article is being re-titled and re-organized in a way that far better represents the overall situation.Lindsay658 22:45, 3 July 2006 (UTC)
thread_title:  Reorganization of article 

message: For the paragraph:
::Type II error, also known as a "error of the second kind", a β error, or a "false negative": the error not rejecting a null hypothesis that is not the true state of nature. In other words, this is the error of failing to accept an alternative hypothesis when you don't have adequate power.
Given that you seem to be saying that:
:(a) null hypothesis is NOT rejected.
:(b) null hypothesis is NOT "the true state of nature",
I am certain that it could easily be re-written in the form:
:the error of accepting a null hypothesis that is not the true state of nature
Also, it seems that "the error not rejecting a null hypothesis" should either be:
:"the error OF not rejecting a null hypothesis", or
:"the error IN not rejecting a null hypothesis". (I suspect it should be the first; i.e., "of").
Best Lindsay658 03:05, 18 August 2006 (UTC)
replies: text: In my opinion, your "the error of not rejecting a null hypothesis when the alternative hypothesis is the true state of nature" has solved everything. Congratulations on a clear mind. Lindsay658 21:44, 18 August 2006 (UTC)
thread_title:  Double negative 

message: An anonyomous editor [63.167.255.231] noted on the main page that:
:Comment: I am not sure if "positive predictive value" is an appropriate method of measurement of security screening measures, because Type I and Type II errors are not of equal importance in this context. Traditional analysis of error rates assumes that Type I and Type II errors are equally undesirable outcomes. However, unlike many other forms of testing and measurement, the purpose of the security screening is, ostensibly, to reduce the incidence of Type II errors to zero or as close to zero as possible. Consequently, the rate of Type I error is of secondary importance. The only time this would be of primary relevance would be if you were comparing two or more different screening protocols, all of which identical Type II error rates, but each of which had a different Type I error rate. The protocol that had the lowest incidence of Type I error would obviously be preferable under these circumstances, but of course this assumes complete equivalence among all of the measured protocols in eliminating Type II errors to begin with.
I have moved the comment here because comments don't belong on the main page. The editor here has some justice behind his comments. Indeed, this is a problem of decision theory since not only must the Type I/II error rate be taken into account, but also the loss function that applies, since the loss on detaining a harmless passenger is much less than the loss on allowing a terrorist passenger on board.
I think that the right way to approach this in the main article is to point out (and link to) the connection to decision theory, perhaps using this example as a springboard. But since this article is explaining only Type I/II errors, it isn't the place to give a full explication of decision theory. Perhaps this can be added to the list of "to-dos". Bill Jefferys 23:55, 21 August 2006 (UTC)
thread_title:  Security Screening 

message: I removed the statement that size is equal to power. This is wrong; in fact size is the maximum probability of Type I error; that is, the smallest level of significance of a test. In many cases, size is equal to \alpha (for example a test statistic T having a continuous distribution and point null hypothesis), but in general it is \operatorname{sup}\{\operatorname{Pr}\left[T \ge c\mid\theta\right] : \theta\in\Theta_0\} where c is the critical value. Btyner 22:11, 3 November 2006 (UTC)
thread_title:  size 

message: Once again, the truth table got changed to an invalid state. The problem is obviously that True and False are used here in two different senses (i.e. the name of the actual condition and the validity of the test result); and so some people look at the intersection of the False column and the Negative row and think the cell should contain "False Negative". I've tried to make this blatantly clear, though some will think this solution is belabored. If you can think of a better way to get this idea across, please leap in. Trevor Hanson 05:19, 13 December 2006 (UTC)
thread_title:  Truth table confusion 

message: There seem to be some inconsistant use of capitalization of the word type in this article. It seems there is confusion on the web too. Does any one know if 'type' should be capitalized if appearing in the middle of a sentence? 194.83.138.183 10:40, 8 March 2007 (UTC)
thread_title: Capitalization of the terms type I and type II errors

message: This article, currently, is very math and statistics heavy and is not useful as a cross reference from other articles that talk about false negatives and false positives.—C45207 | Talk 20:45, 12 March 2007 (UTC)
thread_title:  Statistics Heavy 

message: From the article:
Type II error, also known as an "error of the second kind", a β error, or a "false negative": the error of accepting a null hypothesis when the alternative hypothesis is the true state of nature.
It's my understanding that in a hypothesis test, you never accept the null. You either reject the null (i.e. accept the alternative) or you refrain from rejecting the null. So the above sentence incorrectly defines "type II error". However, a "false negative" can indeed refer to a situation where an assertive declaration is made, e.g. "You do not have cancer."
So a type II error and a false negative aren't necessarily the same thing.
Despite the Wikipedia guideline to be bold, I'm posting this to the talk page rather than making a correction to the article itself, in part because my knowledge of statistics isn't all that deep and there may well be further subtleties here that I'm missing. (In other words, I'd rather risk a type II error than a type I error. ;-) If you can affirm, qualify, or refute what I've said here, please do. Thanks. Fillard 16:40, 4 May 2007 (UTC)
replies: text: I'd agree that i'd prefer "fail to reject the null" to "accept the null" (and i see it's been changed now). However I think the term "false negative" *is* appropriate as the situation in medical testing is pretty much analagous - a negative test result means e.g. "the test produced no evidence that you have cancer". It's a good analogy when the test result is really continuous (e.g. a chemical concentration) and is dichotomised at some cut-off that should be chosen with care. Qwfp (talk) 12:30, 21 January 2008 (UTC)
thread_title:  Distinguishing between type II errors and false negatives 

message: I've made some minor tweaks to the section on null hypotheses. If anyone wants to check out the background to these changes (it necessitated a bit of head scratching and a visit to a library!), there are comments over at Talk:Null hypothesis#Formulation of null hypotheses and User_talk:Lindsay658#Null_hypotheses. -- Sjb90 11:35, 14 June 2007 (UTC)
thread_title:  Null hypothesis tweaks 

message: In one of the elaborations of the presence or absence of errors, the example of pregnancy testing is given.
On the basis that there is a well-established medical/physiological/psychological condition known as "false pregnancy" (see pseudocyesis) I suggest that it would be far better to choose a domain other than pregnancy for the example; because it could well be construed that, rather than testing for the presence/absence of a pregnancy (i.e., the presence of a foetus within the woman's uterus), it was actually testing for the presence/absence of a "false pregnancy" (viz., pseudocyesis with no foetus present). Lindsay658 01:13, 2 August 2007 (UTC)
replies: text: Yes, good point. Somebody added that example long ago, and it seemed clear enough at the time, but something else would be better. I've changed it. Trevor Hanson 03:18, 2 August 2007 (UTC)
thread_title:  Misleading example? 

message: The section "Understanding Type I and Type II Errors" was created by Varuag doos on 10 December 2006  (19:27 edit). The second paragraph, starting with the lead-in "Associated section," was (at the time I deleted it) substantially the same as in that original contribution. To help the discussion, here is the deleted paragraph in its entirety:
:Associated section - Statistics derives its power from random sampling. The argument is that random sampling will average out the differences between two populations and the differences between the populations seen post "treatment" could be easily traceable as a result of the treatment only. Obviously, life isn't as simple. There is little chance that one will pick random samples that result in significantly same populations. Even if they are the same populations, we can't be sure whether the results that we are seeing are just one time (or rare) events or actually significant (regularly occurring) events.
At this point one might have wanted to, say, provide a better segue into this paragraph, getting rid of the awkward "Associated section" lead in. One might also have corrected the fourth sentence (the one beginning with "There is little chance"), which makes it sound as if random samples can "result in" one or another kind of "population" (the term "population" has a technical meaning in statistics and is not synonymous with "statistical sample"). However, my real problem is that I simply don't understand three out of five sentences in this paragraph (I do understand sentences #1 and #3). There are some "differences between two populations" that random samples are supposed to be "averaging out." What are then the differences that persist "post treatment"? (While we're at it, what is this "treatment," enclosed in scare quotes?) I find the whole second sentence (the one beginning with "The argument is") very confusing. And finally, what is the point of this paragraph? Is it simply to warn that Type-I errors are always possible? Or is it that Type-II errors are always possible? In either case, it's too trivial a point to deserve a paragraph. If the intention is to say something else, then the whole paragraph should definitely be re-written by someone who understands what this something else is supposed to be.  Reuqr 05:25, 14 October 2007 (UTC)
thread_title:  Removing the paragraph "Associated section" from the section [[Type_I_and_type_II_errors#Understanding Type I and Type II Errors|Understanding Type I and Type II Errors]] 

message: In qualitity control applications, this is also known as producer's risk (the risk of rejecting a good item) and consumer's risk (the risk of accepting a bad item)  —Preceding unsigned comment added by 70.168.79.54 (talk) 13:48, 29 July 2010 (UTC)
replies: text: I'd agree that "Type I" and "Type II" isn't great terminology. I see little wrong with "false negative" and "false positive" as alternative more descriptive names myself and i've seen them used in medical statistics literature. Qwfp (talk) 12:45, 21 January 2008 (UTC)

text: I completely agree, Type I and Type II are merely legacy terminology, are easily confused, and are really meaningless.  "false negative" and and "false positive" are to the point.  I see no benefit for any other term. Mingramh (talk) 14:09, 2 April 2008 (UTC)
thread_title: Alternate terms for Type I and Type II Error

message: Are the 2 examples under the Computer Security section backwards?Mingramh (talk) 14:15, 2 April 2008 (UTC)
replies: text: I don't see anything backward about them myself. Qwfp (talk) 18:06, 2 April 2008 (UTC)
replies: text: I think I see what Mingramh is saying... it depends on how you define your positive test result. If a positive test indicates an intruder (which would be my intuition for a computer security test), then the example is correct. If a positive test indicates a user, then the example is reverse. I've actually seen "positive" indicating both definitions in this domain. The example given in the computer security section is clear to me because it fits my intuition, but perhaps can be made a little clear for those to whom it is counter-intuitive. If no one wants to try to modify it, I'll take a stab at it soon. WDavis1911 (talk) 23:20, 28 May 2008 (UTC)
thread_title: Computer Security

message: I suggest removing this example table.  I'm not clear on what legal system abjectly uses a test for "innocence" where the presumption (null hypothesis) is on "not innocent".  It could be argued that civil law uses this; however, there it's not really consistent with a hypothesis test at all since the basis is more a "preponderance of doubt".  It could also be argued that the de facto situation in a kangaroo court uses a "not innocent" null hypothesis, but in that case I don't beleive it's consistent with a hypothesis test either since the "truth" is not what they're after.  I never heard of someone being found "not innocent" by even a kangaroo court.  Garykempen (talk) 19:41, 22 March 2009 (UTC)
thread_title: Innocent / Not Innocent

message: I'm going to be studying this article, and in the process will clean up a few minor things. The references to a "person" getting pregnant are silly, for example. Check the wiki page on pregnancy: only women get pregnant. Etc.. Brad (talk) 01:50, 25 August 2008 (UTC)
thread_title: Minor Edits

message: Sensitivity and Specificity are essentially measures of type-I and type-II error - I think the articles could be merged.
Wjastle (talk) 14:43, 10 October 2008 (UTC)
replies: text: - Sensitivity and Specificity are also used to characterize the performance of physical or biological tests. In that case nobody calls them type-I or type-II errors, which is statistical jargon. I think we should leave them separate. Frederic Y Bois (talk) 12:53, 14 October 2008 (UTC)
replies: text: I agree with Frederic. Many people in the health sciences need to learn about sensitivity and specificity but do not need to learn about Type I and Type II errors and would be confused by that nomenclature (which is pretty awful, IMHO, but its far too entrenched to change it). Although the mathematical definitions are closely related the contexts are quite different, so it's less confusing to keep them separate. Qwfp (talk) 23:07, 14 October 2008 (UTC)
replies: text: If this is a vote, I agree with Qwfp and Frederic Y Bois--the terms Type I and Type II errors are widely used in statistics and (at least) the social sciences. Were a student to look up Type I errors and be redirected to Sensitivity, she would be enormously confused, since the concepts are only similar, not identical.--Anthon.Eff (talk) 02:26, 15 October 2008 (UTC)
replies: text: Agreed, definitely no merging. While the concepts are similar they are applied to very different situations.Kjbeath (talk) 05:01, 3 November 2008 (UTC)

text: Strongly oppose suggestion of a merger. In epidemiology and diagnostic testing, the issue is Sensitivity and Specificity, and type I and type II errors are barely mentioned. SNALWIBMA ( talk - contribs ) 11:03, 3 November 2008 (UTC)
replies: text: Strongly oppose: they are used in completely different disciplinary domains, and are used to denote entirely different entities as their respective referents. A merge would simply encourage an unproductive and misleading sloppiness of speech and thought in those who are less better informed and who come to Wikipedia to be set straight.Lindsay658 (talk) 01:09, 9 November 2008 (UTC)
thread_title: Merge proposal

message: Is home testing for aids accurate  —Preceding unsigned comment added by 70.119.131.29 (talk) 23:15, 30 July 2009 (UTC)
thread_title:  aids testing 

message: As far as I remember, the null hypothesis in database searches is that documents are NOT relevant unless proven otherwise. This is thus exactly the opposite of what is said in the article. I tend to remove the whole section, but I think I will wait for some time to see whether citations for one or the other interpretation can be given.194.94.96.194 (talk) 08:43, 9 December 2009 (UTC)
replies: text: I agree. It's garbage. And in any case this specific example of an application of type I/type II errors etc adds nothing of value to the article. Remove the lot. SNALWIBMA ( talk - contribs ) 09:49, 9 December 2009 (UTC)
replies: text: Removed section for reasons stated above since no one has disputed in a year. Dthvt (talk) 21:38, 7 December 2010 (UTC)
thread_title:  computer database searches 

message: Consider your self in a market and you want to buy an apple. Now seeing an apple at fruit shop you have to take decision whether apple is Healthy or Unhealthy? There will be four cases:
# You purchased apple thinking that apple is Healthy and it turns out to be Healthy. Your decision was right. No error in decision.
# You purchased apple thinking that apple is Healthy and it turns out to be Unhealthy. Your decision was wrong. One kind of error in decision.       
# You leave the apple thinking it is is Unhealthy and it turns out to be Healthy. Again your decision was wrong. Other kind of error in decision.
# You left apple thinking that apple is Unhealthy and it turns out to be Unhealthy. Your decision was right. No error in decision.
In case 1 and 4 you made no mistake your decision was correct. But in case 3 and 4 there is error. Error in case 2 is more harmful, it effect you more because you have purchased unhealthy apple. So this is TYPE II error. Where as error of case 3 is not so crucial, is not so harmful. because you have left apple. So this is TYPE I error.
:I moved this from the Lead.  The 4-option matrix is helpful (there's actually a chart of this in the body of the article, maybe it should go there.  It probably doesn't belong in the lead, since there are already examples, but let's discuss that.  Also, whether type I or type II error is more harmful depends on what the null hypothesis is.  Sometimes being too conservative is ok (walking along a cliff), but sometimes being too conservative is very harmful (not pulling the trigger enough).  Sometimes being too open is ok (meeting new friends) but sometimes being too open is very dangerous (falling for cons).  Any example must make clear that context matters.  I'll see if I can incorporate this into the body. Ocaasi (talk) 21:36, 18 March 2011 (UTC)
replies: text:   What makes an apple healthy and unhealthy?  If you don't buy it, you won't know until someone else make a bad decision.  It's a selfish decision example. It takes an objective outsider to help.  Four cases for the individual, extra cases to get the statistical errors.  The simple single event example neglects the multi event rates required to define Type I and Type II errors. Zulu Papa 5 * (talk) 07:37, 20 March 2011 (UTC)
replies: text:  I agree that it's not a great example.  Although, the single simple event is implied to have scale effects; for example, a common type II error, and one used in the article is a legal case, is where the court lets a guilty man go free to avoid the type I error of convicting an innocent one.  That single case is not altogether different from the apple example, and if many such people make many such choices, the error becomes statistically significant.  Maybe I should mention that while single case examples can help illustrate the Types, that they do not become statistically relevant until there is a group of them.  Otherwise, what do you think of the current lead as I updated it yesterday?   Ocaasi (talk) 12:45, 20 March 2011 (UTC)
replies: text:  Defining statistical errors has nothing to do with their relative "harmfulness". We seem to have (too) many good examples already.
Loard (talk) 15:49, 13 April 2011 (UTC)
thread_title:  Apple example 

message: I have removed the following:
::"A false positive in hiring job candidates would mean turning down a good candidate which performed poorly in an interview, while a false negative means hiring a bad candidate that performs well in an interview."
This scenario provided by User 174.21.120.17 is an excellent example of  (so to speak) "perfect choice" consequent upon a "flawed" selection process, and has nothing to do with type I and type II errors.
By contrast, a type I or type II error would be one that is consequent upon a flawed selection choice that was made from a (so to speak) "perfect" selection process.Lindsay658 (talk) 22:32, 27 March 2011 (UTC)
thread_title:  Interview 

message: There are two articles discussing related, but non-synonymous terms: this one and Sensitivity and specificity. Consensus (look: Merge proposal) seems to be that thay should stay separate. This article takes care of technical side of things (formulas, alternative definitions), while Sensitivity and specificity discusses issues arrising in the application of statistical methods to fields such as medicine, physics and "airport security check science". I have edited the summary accordingly, also adding (a little clumsy) "about" template redirecting "applied" readers to Sensitivity and specificity. The rest of the article should follow.
I would like to discuss following improvements:
# My introduction
# Creation of new paragraph "Definitions", which would clearly link (also using aforementioned table) often confused, but related terms such as type I and II errors, false negative, positive, alpha, beta, sensitivity, specificity, false positive and negative rate etc - this should of course not appear in specificity and sensitivity, where application has the priority
# Clean-up of "Further extension" - some examples are anecdotal [Type III errors should be elucidated in more detail, especially with relation to drawing true conclusion from false premisses as a definition of invalid argument]
# Stressing that talking about type I and type II errors is meaningless without knowing "the real state of things" (we need a clear term for this one too) and clear relation to null hypothesis; as well as that null hypothesis is never accepted, one can only fail to reject it
# Rewriting of "Statistical error" in a non-textbook manner
# Accomodation of true positives and true negatives (false/true negatives/positives all redirect here, maybe 'trues' should redirect to Statistical hypothesis testing?)
# Expansion of the table under type II error to include "true positive" and "true negative" under "right decision"
# Merging of "Consequences" paragraph with "Examples"
# Shortening of "Examples" (they should be more prominent in specificity and sensitivity)
# Removing "false positive rate", "false negative rate", "null hypothesis", "Bayes' theorem" and "error in science" from their respective paragraphs and putting them into "definitions" in another, proper place
Loard (talk) 17:47, 13 April 2011 (UTC)
thread_title: Reorganization 2011

message: While there are portions of great substance in this article, overall it leaves this reader -- who has seen many presentations of this subject over the years, and three further today -- with the impression of a flailing about with the subject, or perhaps experimenting with it.  It is like a teacher converting their lecture notes into a textbook after the first time teaching through a course, rather than after teaching the subject for many years.
So, though rarely would I say this, I feel less solidly informed on this subject (honestly, feeling more confused) after perusing the wiki article than I did when first coming to the site (after having looked at 2-4 min YouTube videos to see state of that pedagogic art).  Here, one is left with the sense of the results of gulping large mouthfuls and swallowing with only brief chewing -- which gets the job done, but is not great in terms of nutrition (or avoiding GI discomfort).  I ascribe the impression as likely being due to the polyglot authorship.
So, I'd suggest that the wiki group that's created this page:
 
(1) Agree to have one significant and authoritative contributor take on the task of a major edit; 
(2) That the individual do that major edit by first distilling the article down to the best available single pedagogic approach to presenting the T-1/T-2 definitions and fundamentals, **including standard graphics/tables the best teachers use** (!); 
(3) Expanding to add a second standard but alternative (textbook-worthy) approach to the explanation, but one clearly tied to the first approach so that readers get two different attempts ("angles") at explaining the fundamental concepts; 
(4) That next provided might be **limited**, clear, authoritative set examples, with focus on standard examples appearing in at least one major source text, which can then be cited so that deeper discussion and understanding can be pursued; 
(5) That all other extraneous subject matter be limited or eliminated, e.g., in taking the reader from standard Type I/II thinking to the current state of the statistical art (what most statisticians believe and apply, right now), rather than listing encyclopedically every additional proposed error type, even the humorous -- one short insight-filled paragraph (please!); 
(6) That historical aspects be reduced to one such brief  paragraph, rather than as an alternative parallel development of the fundamentals; (Every chemist mentions its origin with Kekule when teaching about benzene's aromaticity; no one spends any more than a line or two on it, because our understanding of the concept is all the deeper for the passed time, and because the target audience for his writing is not like current audiences in any way; and
(7) That referencing and notes be reviewed for focus, relevance, and scholarly value; e.g., the citation of the high school AP stats prep website should probably go.
These are my suggestions as a teacher and writer and as a knowledgable outsider on this, and is prompted by my inability to suggest the article, as is, to young people needing to understand these errors.  
Note: The only thing I did editorially was to remove as tangential the reference to types of error in science, because that section begged the question of the relation of those types to this articles' Types I and II error, where connection (explanatory text) was completely missing. (!)
Prof D.Meduban (talk) 00:45, 9 June 2011 (UTC)
replies: text: Thanks for your insightful comments.  One key point.  Wikipedia functions as a reference encyclopedia not a pedagogic tool.  We include all relevant aspects and do not do so in a how-to format.  You can help us improve specific examples and phrasing if you like, but please take note of the difference between this approach and a teaching guide.  Cheers, Ocaasi t | c 02:35, 9 June 2011 (UTC)

text: I also find the article confusing (as most of statistic textbooks). The hypothesis is a set of parameters, and the probabilities \alpha and \beta depend hence on the parameter. I think \alpha and \beta should be bounds of the probabilities, not probabilities. Sophia.Orthoi (talk) 22:49, 20 November 2023 (UTC)
thread_title:  Problematic article, overall 

message: the type I and type II definition are totally wrong according to more books and otherwebsites  — Preceding unsigned comment added by K2k1984 (talk • contribs) 10:05, 15 July 2011 (UTC)
replies: text: Hopefully the parts you are referring to are now correct. Kernel.package (talk) 06:56, 13 October 2011 (UTC)

text: "A statistical test can either reject (prove false) or fail to reject (fail to prove false) a null hypothesis, but never prove it true (i.e., failing to reject a null hypothesis does not prove it true)"  — Preceding unsigned comment added by 97.251.38.192 (talk) 12:36, 9 November 2012 (UTC)
thread_title:  this concept is totally wrong  

message: I rewrote the introduction,  firstly because the term "false positive" and "false negative" come from other test areas, and are not specifically used in statistical test situations. And secondly the example that was given, could hardly be understood as an example in a statistical test.   Nijdam (talk) 09:15, 13 October 2011 (UTC)
thread_title:  New intro 

message: :I've the impression that big parts of the article treats type I and type II errors as synonymous with false positives and false negatives. Imo they belong to different fields of research.Nijdam (talk) 11:15, 14 November 2011 (UTC)
thread_title: Related terms

message: In the section "Understanding Type I and Type II errors," the last sentence includes "3.4 parts per million (0.00002941 %)," in which the parenthetical percentage seems to be in error. If you calculate 3.4/1000000, the answer is 3.4E-6, or 0.00034%. Gilmore.the.Lion (talk) 15:06, 21 October 2011 (UTC)
thread_title:  3.4 Parts Per Million 

message: As the original contributor, I have written a new introduction, and have moved the previous introduction down the page. I think that, as I have written it, most of the "complaints" about the technical complexity of the article will stop -- given that we can now say that the other stuff is connected with the imprecise catachrestical extension of the technical terms (by people that are too lazy to create their own) into these other areas in which, it seems, it is not desirable to speak in direct plain English, and that one must be as distant, obscure, and as jargon-laden as possible.Lindsay658 (talk) 00:48, 21 December 2011 (UTC)
thread_title:  New Introduction 

message: There has been editing over and over about the example of the wolf.
:H_0: "no wolf"
Is rejected when some-one cries "wolf'.
:Type I error is made when H_0 is rejected and there is actually no wolf.
:Type II error is made when H_0 is not rejected but there is a wolf.
So far so good. But what about the terms (excessive) "skepticism" and "credulity". Normally a test is skeptical, i.e. one only beliefs there is a wolf if one sees one. Because of this skepticism a type I error is not made easily. As a consequence a type II error is at hand, and may only be avoided by choosing for credulity, i.e. choosing a reliable investigator, who only cries "wolf" when he is pretty sure there is one. If we want to use the words credulity and skepticism in connection with the types of error, we may say: as a consequence of the "credulity" an error of type I is sometimes made, and as a consequence of the "skepticism" an error of type II is sometimes made. Nijdam (talk) 07:58, 12 April 2012 (UTC)
replies: text:  The reference given does not say that Type I error is an "error of credulity." It discusses these terms in the extremes: "believe everything!" and "believe nothing!" The phrase "Type I error is an error of credulity" is vague - does it mean that one makes an error by believing that the null is true? That is not Type I error. Also an "error of credulity" could go either way - too much or too little. There is no good way to fix these sentences using the same words because they can be interpreted different ways. What is an "error of skepticism"?  Was one too skeptical? Not skeptical enough? Skeptical when they should have been credulous? Finally I cannot see why anyone would want to rely on an "Encyclopedia of Pseudoscience" for a definition of a statistical concept. I could find ten other good definitions in statistical references. I don't think that we want pseudoscience but rather science. Mathstat (talk) 19:23, 12 April 2012 (UTC)
thread_title:  Wolf 

message: A few years ago, I came up with a mnemonic device to help people remember what Type I and Type II errors are. Just remember "apparition" and "blind".
Apparition starts with an "A", as does "alpha, which is another term for Type I error. An apparition is a ghost, i.e. you're seeing something (a defect or a difference) that isn't there.
Blind starts with a "B", as does beta, which is another term for Type II error. Blind means you're not seeing something (a defect or a difference) that is there.  — Preceding unsigned comment added by WaltGary (talk • contribs) 08:38, 6 October 2012 (UTC)
replies: text: Thanks, that is a good mnemonic device, maybe it could be added to the <<Memory formulas>> in the box?
Anyways: I think there is a real issue with the <<Memory formulas>> in the Table of error types. It seems totally confusing if you look at the table and read off the intersections (H0) Valid/True and (H0) reject which is the Type 1 error, but in the formula it says: "Type-1 = False result but accept it". This seems to be talking about the H1 Hypothesis in each case. To clarify that it should def. be added in front like "Type-1 = H1 false, but accept it" , "Type-2 = H1 true but rejected it", or changed completely. What do others think? best --Daimpi (talk) 14:32, 7 November 2015 (UTC)

text: Nice. Of course this would be unnecessary if everybody used "false positive" and "false negative" instead of the stupid and dull labels "I" and "II". Anybody in favor of making this article a redirect to False positives and false negatives? Which of those labels is actually more common, the sensible one or the unimaginative one? --Hob Gadling (talk) 17:42, 30 September 2018 (UTC)
thread_title:  Mnemonic Device to remember Type I and Type II Errors 

message: I personally do not agree about the section called : Consequences. Where the article discusses NASA. 
You guys wrote:
"For example, NASA engineers would prefer to throw out an electronic circuit that is really fine (null hypothesis H0: not broken; reality: not broken; action: thrown out; error: type I, false positive) than to use one on a spacecraft that is actually broken (null hypothesis H0: not broken; reality: broken; action: use it; error: type II, false negative). In that situation a type I error raises the budget, but a type II error would risk the entire mission." 
I thought the  definitions are the following:
Type I error is: rejecting the null hypothesis when it is really true.
Type II error: accepting the null hypothesis when it is really not true.
Null Hypothesis: the hypothesis a researcher is testing is not true, there is no statistical significance.  
Researcher hypothesis: there is a statistical significance.  
Basically the section about NASA could have been interpreted with two scenarios, for instance:
Scenario 1:alternative scenario
Electronic circuit Is good: researcher hypothesis.  
Electronic circuit is not good: Null hypothesis
Type I error:  Electronic circuit is good, when its really not good. Rejecting Null hypothesis.
Type II error: electronic circuit is not good, when its really good. Accepting Null Hypothesis
Scenario 2:Your scenario
Electronic circuit is broken: researcher hypotheis.
Electronic circuit is not broken: Null hypothesis
Type I error: Electronic circuit is broken, when its really not broken.Rejecting Null hypothesis
Type II error: Electronic circuit is not broken, when its really broken.Accepting Null hypothesis
I was totally confused by this section of the article.  Please inform me If I am wrong, or maybe I didn't understand what you meant by this section.  — Preceding unsigned comment added by 67.80.92.202 (talk) 22:26, 3 April 2013 (UTC)
replies: text: You wrote:"Null Hypothesis: the hypothesis a researcher is testing is not true, there is no statistical significance." This is just plain wrong but the NASA example has been removed.. 1. a researcher could be testing the null hypothesis. (But (as of August, 2015) the article explains that it is a "speculative" hypothesis that is being compared to the null hypothesis.) 2. True and False have little place in science,(but unfortunately statistics is midway between science and math, and these terms have STATISTICAL meaning different from their common one). 3.Signficance has a fairly well defined meaning in Statistics. It should not be confused with error (but a discussion of these concepts is far too involved to go into here.) Your use of the term is wrong here - specifically a result can be significant whether or not it confirms or contradicts ANY hypothesis and equally confirmation or negatation of ANY hypothesis can be "not significant".  — Preceding unsigned comment added by 216.96.77.80 (talk) 18:12, 2 August 2015 (UTC)
thread_title:  Consequences section about NASA. 

message: The moral of The Boy Who Cried Wolf is that if you abuse peoples trust they wont trust you when it matters. It is about deliberate deciet not being mistaken (i.e. an error!). It's not intuitive and it's a terrible metaphor. It really doesn't help make it clearer. Why not use an example which is actually about making an error?
Lucaswilkins (talk) 17:05, 25 October 2013 (UTC)
thread_title:  The Wolf Thing Again 

message: Hello fellow Wikipedians,
I have just added archive links to  on Type I and type II errors. Please take a moment to review my edit. If necessary, add  after the link to keep me from modifying it. Alternatively, you can add  to keep me off the page altogether. I made the following changes:
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*Added archive https://web.archive.org/20060909224540/http://www.publichealth.pitt.edu/supercourse/SupercoursePPT/18011-19001/18951.ppt to http://www.publichealth.pitt.edu/supercourse/SupercoursePPT/18011-19001/18951.ppt
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thread_title:  External links modified 

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thread_title:  External links modified 

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thread_title:  External links modified 

message: In the section on "Statistical significance", it currently reads: the higher the significance level, the less likely it is that the phenomena in question could have been produced by chance alone.
I think that should be: the lower the significance level ...
Lasse Kliemann (talk) 07:47, 9 July 2017 (UTC)
thread_title:  Significance Level 

message: The link to de.wikipedia is a wrong one (should be "https://de.wikipedia.org/wiki/Fehler_1._und_2._Art") and the link to simple.wikipedia is missing (even though wikidata has the correct information)  — Preceding unsigned comment added by 194.209.78.123 (talk) 12:26, 22 October 2018 (UTC)
thread_title:  Languages 

message: I've been doing some minor edits on the diagram caption, namely defining the acronyms. Now I realize that the diagram is really 3 diagrams - the "upper diagram" (with the overlapping blue TN bell curve and red TP bell curve), the "lower diagram" (with the y-axis being labelled P(TP), TPR, and the x-axis being labelled P(FP), FPR), and the 2x2 table in the upper right. The caption only describes the top diagram, and there is no explanation whatsoever for the bottom diagram, nor for the 2x2 table in the upper right. I think the diagram should be split up into 3 separate diagrams (upper diagram, lower diagram, and 2x2 table), each being described by a well-written caption. This would be in the spirit of answering the criticism that "This article may be too technical for most readers to understand." 
Also, x, X, Y, and Ŷ are not defined in the caption.  — Preceding unsigned comment added by UpDater (talk • contribs) 21:25, 24 October 2021 (UTC)
thread_title:  Diagram is Really Three Separate Diagrams! 

message: For ease of learning and symmetry of terminology, there has to also exist typification of truths.
Type I truth being the true positive (Power of a test).
Type II truth being the true negative.
Then, "Type I" and "Type II" would have more semantic utility, of being a that "Type I" is about "positives", and "Type II" is about "negatives".  — Preceding unsigned comment added by 78.56.218.15 (talk) 00:24, 9 June 2022 (UTC)
thread_title:  Type I and type II truths