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Talk:Kurtosis

Range of Values Kurtosis can take
I think it would be helpful to be clear what range of values the kurtosis statistic can take. I can infer that there is a lower bound of -2, when the article discusses the binomial distribution being an extreme case; this took a fair bit of reading. There is nothing about an upper bound; presumably one exists else you end up with improper distribution? — Preceding unsigned comment added by 194.176.105.139 (talk) 09:48, 4 July 2012 (UTC)

 * No, there is no upper bound. Try three atoms: two atoms at -1 and +1, of probability 0.01 each, and atom at 0 of probability 0.98. And you'll easily guess how the kurtosis can be arbitrarily large. Boris Tsirelson (talk) 08:22, 25 March 2015 (UTC)

 * The sample kurtosis has an upper bound for distribution drawing from the real numbers. The upper bound can be derived from the binomial distribution. Kurtosis <= n-2+1/(n-1)-3, where n is the sample size Zechmann2 (talk) 23:47, 19 November 2021 (UTC)

What is Gamma?
The author defines kurtosis in terms of Gamma, but fails to define Gamma? Is it the Gamma Distribution? Then why doesn't it have 2 parameters? Is it the Factorial? Then why does it have a non-integer parameter? Is it acceptable to use ambiguous functions in a definition without disambiguating them?

Hsfrey (talk) 00:46, 17 March 2012 (UTC)

Wikipedia inconsistancy
Hi statistic wikipedia folks. In this page the Kurtosis definition has a "-3" in it (because the normal has a Kurtosis of 3 so this definition "normalises" things so to say). Subtracting this 3 is actually a convention, maybe this should be mentioned.

A more important point is that every single page on distributions I've encountered here does NOT include the -3 in the Kurtosis formula given on the right (correct me if I'm wrong? I didn't recalculate them all manually :)). So while this is only a matter of convention, we should at least get wikipedia consistent with its own definition conventions? The easiest way seems adapting the definition in this page.

Regards

woutersmet

The reason for this (I think!) is that people who have contributed to this page are from an econometrics background where its common to assume a conditional normal distribution. Hence the -3. —Preceding unsigned comment added by 62.30.156.106 (talk) 21:45, 14 March 2008 (UTC)

Standardized moment
If this is the "fourth standardized moment", what are the other 3 and what is a standardized moment anyway? do we need an article on it? -- Tarquin 10:39 Feb 6, 2003 (UTC)

 * The first three are the mean, standard deviation, and skewness, if I recall correctly.

 * Actually, the word "standarized" refers to the fact that the fourth moment is divided by the 4th power of the standard deviation. — Miguel 15:53, 2005 Apr 19 (UTC)

 * Thank you :-) It's nice when wikipedia comes up with answers so quickly! -- Tarquin 11:04 Feb 6, 2003 (UTC)

 * I think the term "central moments" is also used. See also http://planetmath.org/encyclopedia/Moment.htm

 * No, central moments are distinct from standardized moments. --MarkSweep (call me collect) 02:14, 6 December 2006 (UTC)

Peakedness
Kurtosis is a measure of the peakedness ... so what does that mean? If I have a positive kurtosis, is my distribution pointy? Is it flat? -- JohnFouhy, 01:53, 11 Nov 2004

 * I've tried to put the answer to this in the article: high kurtosis is 'peaked' or 'pointy', low kurtosis is 'rounded'. Kappa 05:15, 9 Nov 2004 (UTC)

It has been pointed out that kurtosis is not synonymous with shape or peakedness, even for symmetric unimodal distributions, please see: 1) A common error concerning kurtosis, Kaplansky - Journal of the American Statistical Association, 1945 2) Kurtosis: a critical review, Balanda, HL MacGillivray - American Statistician, 1988 —Preceding unsigned comment added by Studentt2046 (talk • contribs) 16:27, 10 March 2009 (UTC)

Just backing up that we should not describe Kurtosis as "peakedness", see "Kurtosis as Peakedness, 1905–2014. R.I.P." in The American Statistician 11 Aug 2014 — Preceding unsigned comment added by 130.225.116.170 (talk) 09:33, 2 October 2014 (UTC)

Unfortunately, I couldn't find a citeable source for this, but I suspect that the use of the term "peakedness" is in part driven by surface metrology; if a surface has sharp peaks, its height distribution will have a pronounced tail and therefore high curtosis. I have seen "kortosis=peakedness" quite often in books on surface metrology. I will try to find more on that. 2A02:8071:28A:7700:4125:57A9:C1EB:6C80 (talk) 23:36, 7 March 2021 (UTC)

(Note: In whatever field of study, metrology, imaging, finance, or whatever, the beta(.5,1) distribution provides a canonical example to demonstrate that a sharp peak does not imply higher kurtosis.) — Preceding unsigned comment added by BigBendRegion (talk • contribs) 16:28, 5 June 2021 (UTC)

Peakedness, as I understand, is not intended to imply anything about the shape of the _distribution_, which the cited statistical reference above suggests. Such notions are just plain wrong, but that is not how it is supposed to be interpreted. The term implies that peaks in e.g. a time signal will contribute to a higher kurtosis value (because of the fourth moment, and _yes_, outliers such as peaks in a time signal _do_ contribute to higher kurtosis values). That is, if you want to compare signals (data series) with respect to how much peaks they contain, kurtosis may be used as a measure. Kurtosis has for instance been used as a complement to equivalent SPL (i.e. the average RMS) to catch sound signals with a lot transients/peaks in cases where they don't contribute much to the SPL level. An example of such "correct" usage of peakedness/kurtosis can e.g. be found here: https://www.bksv.com/media/doc/bo0510.pdf. Therefore, it is misleading to state "scaled version of the fourth moment of the distribution. This number is related to the tails of the distribution, not its peak;[2] hence, the sometimes-seen characterization of kurtosis as "peakedness" is incorrect." (quotation from this Wiki article). This is ONLY a proper conclusion if the peakedness is supposed to refer to the shape of the _distribution_, which, as far as I know, is _not_ the intended meaning. Instead of saying it is "incorrect", the text should just explain that peakedness does not suggest that higher kurtosis corresponds to more or higher peaks in the statistical distribution, but it suggest that higher kurtosis suggest more/higher peaks in an associated data _series_ (e.g. a time series). — Preceding unsigned comment added by 79.136.121.89 (talk) 08:19, 20 September 2023 (UTC)

Mistake
I believe the equation for the sample kurtosis is incorrect (n should be in denominator, not numerator). I fixed it. Neema Sep 7, 2005

Ratio of cumulants
The statement, "This is because the kurtosis as we have defined it is the ratio of the fourth cumulant and the square of the second cumulant of the probability distribution," does not explain (to me, at least) why it is obvious that subtracting three gives the pretty sample mean result. Isn't it just a result of cranking through the algebra, and if so, should we include this explanation? More concretely, the kurtosis is a ratio of central moments, not cumulants. I don't want to change one false explanation that I don't understand to another, though. Gray 01:30, 15 January 2006 (UTC)
 * After thinking a little more, I'm just going to remove the sentence. Please explain why if you restore it. Gray 20:58, 15 January 2006 (UTC)

Mesokurtic
It says: "Distributions with zero kurtosis are called mesokurtic. The most prominent example of a mesokurtic distribution is the normal distribution family, regardless of the values of its parameters." Yet here: http://en.wikipedia.org/wiki/Normal_distribution, we can see that Kurtosis = 3, it's Skewness that = 0 for normal. Agree? Disagree?

 * Thanks, that's now fixed. There are two ways to define kurtosis (ratio of moments vs. ratio of cumulants), as explained in the article.  Wikipedia uses the convention (as do most modern sources) that kurtosis is defined as a ratio of cumulants, which makes the kurtosis of the normal distribution identically zero. --MarkSweep (call me collect) 14:43, 24 July 2006 (UTC)

Unbiasedness
I have just added a discussion to the skewness page. Similar comments apply here. Unbiasedness of the given kurtosis estimator requires independence of the observations and does not therefore apply to a finite population.

The independent observations version is biased, but the bias is small. This is because, although we can make the numerator and denominator unbiased separately, the ratio will still be biased. Removing this bias can be done only for specific populations. The best we can do is either: 1 use an unbiased estimate for the fourth moment about the mean, 2 use an unbiased estimate of the fourth cumulant, in the numerator; and either: 3 use an unbiased estimate for the variance, 4 use an unbiased estimate for the square of the variance, in the denominator.

According to the article, the given formula is 2 and 3 but I have not checked this. User:Terry Moore 11 Jun 2005

So who's Kurt?
I mean, what is the etymology of the term? -FZ 19:48, 22 Jun 2005 (UTC)

 * It's obviously a modern term of Greek origin (κυρτωσις, fem.). The OED gives the non-specialized meaning as "a bulging, convexity". The Liddell-Scott-Jones lexicon has "bulging, of blood-vessels", "convexity of the sea's surface" and "being humpbacked". According to the OED (corroborated by "Earliest Known Uses of Some of the Words of Mathematics" and by a search on JSTOR), the first occurrence in print of the modern technical term is in an article by Karl Pearson from June 1905. --MarkSweep 21:05, 22 Jun 2005 (UTC)

Kurtosis Excess?
I've heard of "excess kurtosis," but not vice-versa. Is "kurtosis excess" a common term? Gray 01:12, 15 January 2006 (UTC)

Diagram?
A picture would be nice ... (one is needed for skewness as well. I'd whip one up, but final projects have me beat right now. 24.7.106.155 08:27, 19 April 2006 (UTC)

 * The current picture is nice because it shows real data, but it has some problems:

 * 1) it does not cite any references for the source of the data
 * 2) it is not what we need here: kurtosis comes along when variance is not enough, so for a real case to be interesting one should find a situation where two distributions with the same mean and variance are symmetric or similarly asimmetric (null or same skewness) and yet have a different kurtosis; is this the case here? I am not sure, as no mention of variance is made in the comment to the picture
 * 3) it is not in vector form (SVG) --Pot (talk) 13:21, 18 December 2008 (UTC)

The picture is taken from in my doctoral thesis ("A.Meskauskas.Gravitorpic reaction: the role of calcium and phytochrome", defended in 1997, Vilnius State University, Lithuania). I added this note to the picture description in commons. The picture represents my own experimental data but the dissertation should be considered a published work. The real experimental data cannot be "adjusted" in any "preferred" way but in reality likely no scientist will ever observe an "absolutely clean" effect that only changes kurtosis and nothing else. Audriusa (talk) 14:41, 19 December 2008 (UTC)

 * And in fact kurtosis is rarely used in real experimental data. One of the fields where it is used is for big quantities of experimental data that would seem well modelled by a Gaussian process. If the weight of the tails turns out out be important, then a kurtosis estimate can be necessary. It allows one to take apart a Gaussian from something that resembles it. As I said above, this mostly makes sense when comparing distributions with same mean, variance and skewness. And, if you want to give an example, I argue that this is indeed necessary. So I think that your example is illustrative only if the two variances are equal. Can you tell us if this is the case? --Pot (talk) 16:50, 19 December 2008 (UTC)

 * The dispersion changes from 21.780 (control) to 16.597 (far red). The mean, however, does not change much if to take the +- intervals into consideration (from 10.173 +- 0.975 to 8.687 +- 0.831). So, if comparing only the mean, we would likely conclude that the far red light has no any effect in experiment. But the histograms do look very different. One of the possible explanations can be periodic oscillations around the mean value in time (when the experiment gives the "momentary picture"). Far red light may stop these oscillations, making the output more uniform. Audriusa (talk) 20:38, 20 December 2008 (UTC)

 * Thank you for clarifying this. However, as I pointed out above, once you have two distributions with the same mean, you start considering higher moments. The first one above the mean is variance.  Only if variances are equal you resort to using even higher moments; this is not very common, as the higher the moment the more sensitive to noise. And in practice using moments higher than the variance with few samples is not very significant. So, once again, are the variances equal for the two cases you proposed as an illustration? --Pot (talk) 14:27, 21 December 2008 (UTC)
 * From your talk just comes that the higher moments should only be compared if the lower moments are equal. This is a simple and clear sentence. How sure you are about this? Any references? Audriusa (talk) 17:59, 30 December 2008 (UTC)
 * Google books may let you browse this. It is the famous handbook "Numerical Recipes in C" (but the Fortran version contains the same text). The issue is that higher moments are generally less robust than lower moments, because they involve higher powers of the input data. The advice given in the book is that skewness and kurtosis should be used with caution or, better yet, not at all. More specifically, the skewness of a batch of N samples from a normal distribution is about $$\sqrt{15/N}.$$  The book goes on suggesting that In real life it is good practice to believe in skewnesses only when they are several or many times as large as this.  Here we are speaking about kurtosis, for which the relevant figure is $$\sqrt{96/N}.$$  For the example figure that you added, this means that the difference on sample kurtoses can be considered significant only if $$0.05+0.20 \gg \sqrt{96/N} \Rightarrow N \gtrsim 10000.$$ Even if this is the case, resorting to higher moments, which are inherently less robust, will only be justified where lower moments cannot do the job. I think that adding a section both in skewness and kurtosis explaining these concepts is a good idea. Pot (talk) 12:54, 8 January 2009 (UTC)

Range?
Is the range -2, +infinity correct? why not -3, +infinity?

 * Yes, the range is correct. In general all distributions must satisfy $$\gamma_2 - \gamma_1^2 + 2 \geq 0$$. The minimum value of $$\gamma_2$$ is −2. --MarkSweep (call me collect) 02:26, 6 December 2006 (UTC)

 * I take that back, will look into it later. --MarkSweep (call me collect) 09:32, 6 December 2006 (UTC)

I corrected the french article, which given 0, +infinity for kurtosis ( so -3, +infinity for excess kurtosis). The good range for kurtosis are : 1, +infinity and for the excess kurtosis : -2, +infinity

Very simple demonstration :

$$ \gamma_2=\frac{\mathbb{E}\left[(X-\mu)^2(X-\mu)^2\right]}{\sigma^4} $$

We have

$$ \gamma_2=\frac{\mathbb{E}\left[(X-\mu)^2\right]\mathbb{E}\left[(X-\mu)^2\right]}{\sigma^4}+\frac{\operatorname{cov}\left[(X-\mu)^2,(X-\mu)^2\right]}{\sigma^4}, $$

or

$$ \mathbb{E}\left[(X-\mu)^2\right]=\sigma^2, $$

so

$$ \gamma_2=1+\frac{\operatorname{var}\left[(X-\mu)^2\right]}{\sigma^4}, $$

with $$\alpha^2=\frac{\operatorname{var}\left[(X-\mu)^2\right]}{\sigma^4}\geq 0$$, we have

$$ \gamma_2=1+\alpha^2 \geq 1 $$

This demonstration can be realize with Jensen's inegality (add 10/16/09).

Jensen's Inegality :

$$ \mathbb{E}\left[f(X)\right]\geq f\left(\mathbb{E}[X]\right). $$

We have

$$ \mathbb{E}\left[X^4\right] \geq \left(\mathbb{E}\left[X^2\right]\right)^2, $$

$$ \mathbb{E}\left[X^4\right] \geq \left(\sigma^2\right)^2. $$

so

$$ \frac{\mathbb{E}\left[X^4\right]}{\sigma^4} = \gamma_2 \geq 1. $$

Thierry —Preceding unsigned comment added by 132.169.19.128 (talk) 08:22, 4 June 2009 (UTC)

Sample kurtosis
Is the given formula for the sample kurtosis really right? Isn't it supposed to have the -3 in the denominator? --60.12.8.166

In the discussion of the "D" formula, the summation seems to be over i terms, whereas the post lists: "xi - the value of the x'th measurement" I think this should read: "xi - the value of the i'th measurement of x" (or something close) --Twopoint718 19:25, 13 May 2007 (UTC)

Shape
In terms of shape, a leptokurtic distribution has a more acute "peak" around the mean (that is, a higher probability than a normally distributed variable of values near the mean) and "fat tails" (that is, a higher probability than a normally distributed variable of extreme values)

Is that right? How can a function have both a greater probability near the mean and a greater probability at the tails? Ditto for platykurtic distributions--DocGov 21:49, 18 November 2006 (UTC)

 * Yes, that's right. One typically has in mind symmetric unimodal distributions, and leptokurtic ones have a higher peak at the mode and fatter tails than the standard normal distribution. For an example have a look at the section on the Pearson type VII family I just added. --MarkSweep (call me collect) 02:29, 6 December 2006 (UTC)

 * On the other hand, the Cauchy distribution has a lower peak than the standard normal yet fatter tails than any density in the Pearson type VII family. However, its kurtosis and other moments are undefined. --MarkSweep (call me collect) 04:00, 6 December 2006 (UTC)

 * Another explanation: it's not just peaks and tails, don't forget about the shoulders. Leptokurtic density with a higher peak and fatter tails have lower shoulders than the normal distribution. Take the density of the Laplace distribution with unit variance:

 * $$f(x) = \frac{1}{\sqrt{2}} \exp(-\sqrt{2}|x|) \!$$

 * For reference, the standard normal density is

 * $$g(x) = \frac{1}{\sqrt{2\pi}} \exp(-x^2/2) \!$$

 * Now f and g intersect at four points, whose x values are $$\pm\sqrt{2}\pm\sqrt{2-\ln\pi}$$. Focus on three intervals (on the positive half-line, the negative case is the same under symmetry):

 * Peak $$(0, \sqrt2-\sqrt{2-\ln\pi}) \approx (0, 0.49)$$ Here the Laplace density is greater than the normal density and so the Laplace probability of this interval (that is, the definite integral of the density) is greater (0.25 vs. 0.19 for the normal density).
 * Shoulder $$(\sqrt2-\sqrt{2-\ln\pi}, \sqrt2+\sqrt{2-\ln\pi}) \approx (0.49,2.34)$$ Here the normal density is greater than the Laplace. The normal probability of this interval is 0.30 vs. 0.23 for the Laplace.
 * Tail $$(\sqrt2+\sqrt{2-\ln\pi},\infty) \approx (2.34,\infty)$$ Here the Laplace density is again greater. Laplace probability is 0.02, normal probability is 0.01.

 * Because we focus on the positive half-line, the probabilities for each distribution sum to 0.5. And even though the Laplace density allocates about twice as much mass to the tail compared with the normal density, in absolute terms the difference is very small. The peak of the Laplace is acute and the region around it is narrow, hence the difference in probability between the two distributions is not very pronounced. The normal distribution compensates by having more mass in the shoulder interval (0.49,2.34). --MarkSweep (call me collect) 08:57, 6 December 2006 (UTC)

Looking at the Pearson Distribution page - isn't the example a Pearson V, not Pearson VII as stated in the title? And, if not, where is more info on Type VII - the Pearson Wikipedia page only goes up to V. 128.152.20.33 19:34, 7 December 2006 (UTC)

 * Obviously the article on the Pearson distributions is woefully incomplete. As the present article points out, the Pearson type VII distributions are precisely the symmetric type IV distributions. --MarkSweep (call me collect) 05:35, 8 December 2006 (UTC)
 * Hello Mark, I know it's 16 years since you wrote your comments, but I just want to say that I believe I understand what kurtosis is, and what you say is very good: the Laplace example is very good, one typically has in mind symmetric unimodal distributions is a good point (well, unimodal goes only with leptokurtic, right?), and the peak - shoulders - tails remark is spot-on (btw I think of this as head - shoulders - arms). People who say leptokurtic means fatter tails but not more peakedness obviously don't understand that the one is not possible without the other. The article has a lot crap statements, because some Wikipedians blindly believe what is written in Scientific American or in their favourite book. Okay, enough ranted, thanks again for your words of reason. --Herbmuell (talk) 20:49, 22 March 2022 (UTC)

(Herbmuell's comment "one is not possible without the other" is false. Take the beta(.5,1) distribution and reflect it around the origin. The new distribution is (1) symmetric and (2) infinitely peaked, but (3) it is light tailed (kurtosis <3). For another example, take the U(-1,1) distribution and mix it with the Cauchy, with mixing probabilities .99999 and.00001. The resulting distribution is (1) symmetric and (2) appears perfectly flat over 99.999% of the observable data, but (3) has fat tails (infinite kurtosis).)BigBendRegion (talk) 15:42, 20 June 2022 (UTC)

Was someone having us on ? (hoax)
"A distribution whose kurtosis is deemed unaccepatably large or small is said to be kurtoxic. Similarly, if the degree of skew is too great or little, it is said to be skewicked" – two words that had no hits in Google. I think someone was kidding us. DFH 20:33, 9 February 2007 (UTC)
 * Agree, zero google hits. --Salix alba (talk) 21:24, 9 February 2007 (UTC)

leptokurtic / platykurtic
I think the definitions of lepto-/platy- kurtic in the article are confusing: the prefixes are reversed. I'm not confident enough in statistics to change this. Could someone who understands the subject check that this is the correct usage?

A distribution with positive kurtosis is called leptokurtic, or leptokurtotic. In terms of shape, a leptokurtic distribution has a more acute "peak" around the mean (that is, a higher probability than a normally distributed variable of values near the mean) and "thin tails" (that is, a lower probability than a normally distributed variable of extreme values). Examples of leptokurtic distributions include the Laplace distribution and the logistic distribution.

A distribution with negative kurtosis is called platykurtic, or platykurtotic. In terms of shape, a platykurtic distribution has a smaller "peak" around the mean (that is, a lower probability than a normally distributed variable of values near the mean) and "heavy tails" (that is, a higher probability than a normally distributed variable of extreme values).

leptokurtic: –adjective Statistics. 1. (of a frequency distribution) being more concentrated about the mean than the corresponding normal distribution. 2. (of a frequency distribution curve) having a high, narrow concentration about the mode. [Origin: 1900–05; lepto- + irreg. transliteration of Gk kyrt(ós) swelling + -ic]

lepto- a combining form meaning "thin," "fine," "slight"

platykurtic: 1. (of a frequency distribution) less concentrated about the mean than the corresponding normal distribution. 2. (of a frequency distribution curve) having a wide, rather flat distribution about the mode. [Origin: 1900–05; platy- + kurt- (irreg. < Gk kyrtós bulging, swelling) + -ic]

platy- a combining form meaning "flat," "broad".

--Blick 19:43, 21 February 2007 (UTC)

 * The current usage is correct and agrees with other references, e.g. . DFH 21:39, 21 February 2007 (UTC)
 * I don't think that the problem is with the words platykurtic and leptokurtic, which is what your references are to. It's the issue that leptokurtic is described as having heavy tails. The more common explanation is that leptokurtic distributions have thin tails and that platykurtic distributions have heavy tails. Phillipkwood (talk)
 * I'm not sure about the prefixes, but the changes you made earlier today were definitely wrong and did not agree with other outside sources. I wasted a lot of time trying to make sense of it before I noticed your edit, and then I undid it. Tabako (talk) 00:11, 11 November 2008 (UTC)

Well, I think it does agree with outside sources, at least the _American Statistican_ Maybe, to make it less confusing, it's helpful to talk about length (which is what you're talking about) and thinness. Here's a quote:(Kevin P. Balanda and H. L. MacGillivray The American Statistician, Vol. 42, No. 2 (May, 1988), pp. 111-119.) Who write: "Dyson (1943) gave two amusing mnemonics attributed to Student for these names: platykurtic curves, like playpuses, are square with short tails whereas leptokurtic curves are high with long tails, like kangaroos, noted for "lepping" The terms supposedly refer to the general shape of a distribution, withplatykurtic distributions being flat topped compared with the normal, leptokurtic distributions being more sharply peaked than the normal, and mesokurtic distributions having shape comparable to that of the normal. So, yes, "leptokurtic" distributions have long and thin tails, Platykurtic distributions have short heavy tails.). —Preceding unsigned comment added by 128.206.28.43 (talk) 15:56, 11 November 2008 (UTC)

 * I'm still not sure about this. I'm suggesting that instead of describing a platykurtic distribution as one with "thin tails", we should say "broad peak". Would you agree? --Blick 07:30, 5 March 2007 (UTC)

Not really. Moments are more sensitive to the tails, because of the way powers work. The squares of 1, 2, 3 etc. are 1, 4, 9 etc. which are successively spaced farther apart. The effect is greater for 4th powers. So, although the names playkurtic and leptokurtic are inspired by the appearance of the centre of the density function, the tails are more important. Also it is the behaviour of the tails that determine how robust statistical methods will be and the kurtosis is one diagnostic for that.203.97.74.238 00:46, 1 September 2007 (UTC)Terry Moore
 * I agree with Terry, but given the American Statistician terminology, I made a minor edit to the page to reflect this discussion- i.e., that "thin" and "thick" refers to the height of the PDF. Reading the original American Statistician paper, reflects some of the language on this point and this seemed to be the most accurate compromise. I checked the "standard terminology" references above, and nothing is mentioned in those about thickness versus thinness- they're just definitions that all of us seem to agree on. Phillipkwood (talk) 15:00, 8 December 2008 (UTC)
 * Hm. I looked at it and I suggest that:

 * "peak" should be peak → done
 * "fat tail", "thin tail" should be fat tail, thin tail → done
 * fat tail should be a link → done
 * thin tail should be sub Gaussian (not super Gaussian, and without quotes) → done
 * fat tail should be super Gaussian (not sub Gaussian, and without quotes) → done
 * Other than these typographical changes, the terms leptokurtic and mesokurtic should be made consistent in the article and between articles (such as those about fat tail and heavy tail) → done. --Fpoto (talk) 18:37, 8 December 2008 (UTC)

L-kurtosis
I don't have the time to write about that, but I think the article should mention L-kurtosis, too. --Gaborgulya (talk) 01:13, 22 January 2008 (UTC)

why 3?
to find out if its mesokurtic, platykurtic or leptokurtic, why compare it to 3? —Preceding unsigned comment added by Reesete (talk • contribs) 10:18, 5 March 2008 (UTC)

The expected Kurtosis for sample of IID standard normal data is 3 (see the wiki article on the normal distribution for more). We tend to refer to excess kurtosis as the sample kurtosis of a series -3 for that reason.. —Preceding unsigned comment added by 62.30.156.106 (talk) 21:42, 14 March 2008 (UTC)

Bias?
Perhaps the article should include more explicit notes on bias. In particular, I'm wondering why the formula is using biased estimates of sample moments about the mean; perhaps someone more knowledgeable than I might explain why this is the preferred formula? —Preceding unsigned comment added by 140.247.11.37 (talk) 14:30, 25 June 2008 (UTC)

Excess kurtosis - confusing phrasing
The way the "modern" definition is phrased in the article makes it look like $$\gamma_2$$ could be what they're referring to as excess kurtosis.
 * Yes, this is correct.

However, I get the impression that "excess kurtosis" is actually the "minus 3" term. Is this correct? kostmo (talk) 05:57, 25 September 2008 (UTC)
 * It is the expression containing the -3, which is equal to $$\gamma_2$$. I think the phrase is correctly stated. --Pot (talk) 22:27, 3 January 2009 (UTC)

A nice definition
I found this:

k is best interpreted as a measure of dispersion of the values of Z^2 around their expected value of 1, where as usual Z = (X-mu)/sigma

It has been written by Dick Darlington in an old mail thread. It does not account for the -3 used in Wikipedia's article, but it is clear and could be added to the initial definition. --Pot (talk) 10:40, 19 February 2009 (UTC)

Get Better Examples
Okay, I barely understand the statistical part of the article, why do you have to use an example that involves something only botanists and biologists can understand.. I undertsnad that encyclopaedias are supposed to be erudite, but not pedantic. They shouldn't make you have to keep clicking on newer and newer subjects that you have to read up on just so you can understand the one you originally started with. An example in an encyclopaedia is supposed simple and straightforward, something the uninitiated laymen can understand, not something having to do with red-lights and gravitropic celeoptiles. It's the people's encyclopaedia, you don't have to dumb it down to make it more accessible. My point is, get a better visual example for what kurtosis is. —Preceding unsigned comment added by 70.73.34.109 (talk) 10:26, 30 April 2009 (UTC)

I agree - two clear examples, of very high and very low kurtosis, would make this article much clearer, and much easier to understand at a glance. Use a couple of every-day activities to prove the point. 165.193.168.6 (talk) 12:27, 13 August 2013 (UTC)

Glaring Error?
I'm no statistician, but the description of leptokurtosis currently says it has a more acute peak and fatter tails, whereas playkurtosis has a flatter peak and thinner tails. A quick mental diagram demonstrates to me that this is impossible, and the author(s) must have confused the thickness of the tails for the two cases. A leptokurtic curve must have thinner tails and a platykurtic curve must have fatter tails. Unless anyone objects, I'll correct this in a moment. —Preceding unsigned comment added by 194.153.106.254 (talk) 10:33, 23 July 2009 (UTC)
 * No error, the description was correct, I've reverted your change. Just try to read and understand the article, the description is reasonably well done and graphical examples are in place. Next time, please do no change a math description unless you fully understand it. Raising a problem in the discussion page is good, but wait for someone to answer you doubts before editing. --Pot (talk) 14:17, 23 July 2009 (UTC)
 * I suspect this paragraph has been reversed again. It says "In terms of shape, a leptokurtic distribution has a more acute peak around the mean and thinner tails. Looking at the rest of the article, it should says fatter tails, isn't it ? However I don't feel confident enough to change this. -- Pierre  —Preceding unsigned comment added by 160.228.203.130 (talk) 13:52, 17 May 2011 (UTC)
 * Well, since I've noticed the false change was just done today, I've reverted it myself -- Pierre —Preceding unsigned comment added by 160.228.203.130 (talk) 14:00, 17 May 2011 (UTC)

Alternative to -3 for kurtosis!
On Latin wiki page for Distributio normalis you find a recent (2003) scientific paper which rearranges differently the fourth moment to define a number said in English arch (and in Latin fornix) which ranges from 0 to infinity (and for the normal distribution is 1) instead of the quite strange [-2, infinity). by Alexor65 — Preceding unsigned comment added by Alexor65 (talk • contribs) 20:24, 29 March 2011 (UTC)

?Dead link "Celebrating 100 years of Kurtosis"
Link "Celebrating 100 years of Kurtosis" does not work because file has changed address, now it is at least in faculty.etsu.edu/seier/doc/Kurtosis100years.doc Alexor65 —Preceding unsigned comment added by 151.76.68.54 (talk) 21:02, 2 April 2011 (UTC)

First sentence and citation
The first sentence states "In probability theory and statistics, kurtosis is a measure of the "peakedness" of the probability distribution of a real-valued random variable, although some sources are insistent that heavy tails, and not peakedness, is what is really being measured by kurtosis.[1]"

The reference given says, "The heaviness of the tails of a distribution affects the behavior of many statistics. Hence it is useful to have a measure of tail heaviness. One such measure is kurtosis...Statistical literature sometimes reports that kurtosis measures the peakedness of a density. However, heavy tails have much more influence on kurtosis than does the shape of the distribution near the mean (Kaplansky 1945; Ali 1974; Johnson, et al. 1980)."

The reference seems to directly contradict the first sentence. — Preceding unsigned comment added by 140.226.46.75 (talk) 21:26, 7 October 2011 (UTC)

 * Two things need to be distinguished: (i) kurtosis as a general concept or thing that might be measured ... I have 3 stats dictionaries that say that this is essentially "peakedness"; (ii) the specific measure of kurtosis based on centred ordinary fourth (and second moments) ... for which the property that it is more a measure of log-tailedness might well be valid. Overall this article needs to be re-arranged to distinguish these two points, along the lines of what is current in the article Skewness and which includes several different measures of skewness. The use of the SAS reference (alone) is/was unclear as is can be considered correct as stated, since the SAS reference is an example of a "source" that does claim that "heavy tails have much more influence on kurtosis than does the shape of the distribution near the mean". (Thus a ref for the end of the sentence, not the whole thing.) I will add another ref and rearrange the start. Melcombe (talk) 12:03, 10 October 2011 (UTC)

Clarity of Introduction
When I first read through the introduction, I did not understand what it was saying. However, I read through it again, and, perhaps because I picked up on some word I missed the fist time around, the meaning of the introduction became completely clear. As this seems to suggest that understanding the introduction hinges (or at least is heavily dependent on) on noticing and understanding a very small portion of it, I would suggest that a small, one-sentence "introduction introduction" be added above (or included in) the current introduction, such that it would quickly convey to readers a general "complexity level" at which the article deals with its subject. To clarify, in this context I am using the phrase "complexity level" to refer to a measure of a work's position on a "sliding scale" of sorts that measures the amount that a work is affected by the general tendency of larger words to become more critical to comprehension as the complexity of a work's subject (among other factors) increases. For instance, a college-level thermodynamics textbook is unlikely to spend the same amount of time leading up to a definition of thermal conduction and insulation that an elementary-level science textbook would. As such, a prior understanding of thermal conduction and insulation becomes more necessary to understand the rest of the book in the college textbook than in the elementary school textbook.

Alternately, the "complexity level" could be reduced, for instance, by using more familiar terms than, also for instance, "peakedness", which, while helping the reader to associate the concept with common phrases such as "highly peaked", could perhaps be moved lower in the introduction (or even put into the article itself) and replaced by another term, such as "sharpness", and reducing the repetition of clauses, such as removing the redundant "just as for skewness" in the second sentence.

Aero-Plex (talk) 17:24, 10 November 2011 (UTC)

(Split due to needing to reset the router)

EDIT: Unfortunately, I do not have the necessary time to read and edit the article now, and probably won't for some time, so I cannot edit the article for now. However, from my brief skim through, I did notice that 1. there is a noticeable amount of repetition of terminology, which could be improved, 2. no compact, direct description of a graph with high/low kurtosis is made in the text, and I could only find one by looking in the image descriptions, which may not be noticed by some (I suggest that a sentence along the lines of "High kurtosis causes narrow curves, while low kurtosis causes wide graphs." be added somewhere in the article where it would be noticed), and 3. the "coin toss" example could be better elaborated on, as it seems like it could be very helpful, especially for people who are only coming to this page for a quick summary.

Aero-Plex (talk) 17:41, 10 November 2011 (UTC)

The usual estimator of the population kurtosis
I just conducted a simulation study which seems to confirm that "The usual estimator of the population kurtosis" is in fact an estimator for excess curtosis. Which seems to make sense given the lest part of the formula -3*X — Preceding unsigned comment added by 83.89.29.84 (talk) 23:28, 24 January 2012 (UTC) ALSO: it claims that the estimator is used in Excel, however the excel formula seems to use standard deviation rather than variance: http://office.microsoft.com/en-us/excel-help/kurt-HP005209150.aspx BUT the wiki artickle claims it must be the unbiased standard deviation estimator, which i dont believe exist.. — Preceding unsigned comment added by 83.89.29.84 (talk) 00:11, 25 January 2012 (UTC)

wrong wrong wrong about peakedness
As has been pointed out above, kurtosis is NOT an accurate measure of peakedness. This should be obvious by looking at a graph of the Student's t-distribution with degrees of freedom above 4 and trying to see if you can see anything approaching sharp peakedness as the d.o.f. drops down to 4 and the kurtosis shoots up to infinity. Similarly, look at the gamma distribution graph and try to notice any correlation at all between the sharpness and softness of the peak when k > 1 and the smallness of k (higher kurtosis). The point is that kurtosis measures ONLY heaviness of the tails — and contrary to the former text, there's no difference in this respect between Pearson's kurtosis and excess kurtosis. (Nor can there be, since the two are identical save for being shifted by 3). In fact, it should be obvious that heavy tails and sharp peaks CANNOT in general be correlated -- i.e. could radically change the shape of the peak in the middle of the graph by a strictly local rearrangement of the nearby area while leaving the tails entirely untouched. It's rather sad that a basic article like this had such basic errors for such a long time, but at least they are fixed now. Benwing (talk) 07:10, 23 March 2012 (UTC)

 * OK, it's more complicated than I thought. In fact, Balanda and MacGillivray claim that kurtosis isn't necessarily an accurate measure of tail weight, either, and propose a vague definition of moving probability mass off the shoulders onto the peak and tail, which IMO isn't very helpful intuitively.  I still think the most intuitive interpretation should basically specify tail-heaviness.  I will rewrite the remainder of the article (post-intro) to be more careful about this. Benwing (talk) 08:09, 23 March 2012 (UTC)

I found the following article to be very helpful in explaining the common misconceptions about kurtosis, specifically related to its use a a measure of "peakedness" and "tail weight." (http://www.columbia.edu/~ld208/psymeth97.pdf) Basically, it explains that kurtosis is a movement of mass not explained by the variance. Thus, when we see heavier tails, this means that data points are spread further out, which should lead to an increase in the variance - BUT if the there is also an increase in the number of data points near the mean, this leads to a decrease in the variance; kurtosis is able to explain the change in shape of a distribution when both of these occurrences happen to equality.

I also believe this article makes an important point about the distributions crossing twice, which is helpful to dispel misconceptions about kurtosis.

 * Peakedness (as used in practical applications) is not supposed to imply anything about the statistical distribution: Higher kurtosis suggest more/higher peaks in an associated data series (e.g. a time series). Hence, higher kurtosis implies more "peakedness" in that sense. Unfortunately, this is a concept that had got another interpretation in the statistical community, in which there is a debate of peaks in the distribution (which is a totally different question that results in the conclusion "kurtosis is NOT an accurate measure of peakedness", to which obvioulsly should be appended "of STATISTICAL DISTRIBUTIONS"). But peakedness is not intended to be interpreted in that way when used correctly. See the last paragraph in the discussion above about Peakedness for details. — Preceding unsigned comment added by 79.136.121.89 (talk) 09:46, 20 September 2023 (UTC)

Expand Applications
Section Kurtosis is tagged with ; here are some suggestions: Special:WhatLinksHere/Kurtosis. Fgnievinski (talk) 04:34, 19 January 2013 (UTC)

Add sources from IDRE/UCLA for the various definitions of kurtosis, including citations and which versions SAS, SPSS and STATA use
Here are some sources from IDRE/UCLA for the various definitions of kurtosis, including citations and which versions SAS, SPSS and STATA use ,. Regards, Anameofmyveryown (talk) 18:53, 11 March 2013 (UTC)

Edit protected erroneous reference ?
Hi. Ref. 4 (Pearson, Biometrika 1929) about the lower bound of the kurtosis, is edit protected and it is erroneous, including the doi: anybody can check. Even if the ref to Pearson can be corrected, the previous reference (paper of 2013) which was reverted the 5 February 2014 remains eligible because (a) it applies to a wider class of distributions than the finite discrete ones (because the proof of 2013 use math expectations), and (b) it is issued from a more general inequality applied to d-variate distributions, established in 2004 (ref cited in the 2013 paper). I can send the pdfs of the 2004 and of the 2013 paper to the administrator (please just tell me how to do that) and to interested people. At least please correct the erroneous reference about Pearson, if it is relevant. If it is not possible, please undo the change of the 5 February 2014, and replace ref. 4 by the previous ref 4, which is: Thank you. Michel. About the actual ref 4: Pearson, K. (1929). "Editorial note to ‘Inequalities for moments of frequency functions and for various statistical constants’". Biometrika 21 (1–4): 370–375. doi:10.1093/biomet/21.1-4.361 (1) The toc of Biometrika 1929, 21(1-4) is at: http://biomet.oxfordjournals.org/content/21/1-4.toc I failed to find this paper of Pearson on this toc, and I failed to find it with ZMATH. The doi redirects to the paper of Joanes and Gill, "The Statistician" 1998, vol 47, part 1, pp. 183-189. Indeed it deals with skeweness and kurtosis, but it does not cite Pearson and it does not give a general proof of the inequality valid for any random variable distribution. Anyway there is disagreement between the doi and the ref to Pearson. (2) The 2013 paper is publicly available on http://petitjeanmichel.free.fr/itoweb.petitjean.skewness.html (see ref. 2: "download pdf paper"): see the result top of p.3 and eq. 6. The proof of the more general inequality for random vectors is in my paper: "From Shape Similarity to Shape Complementarity: Toward a Docking Theory." J. Math. Chem. 2004,35[3],147-158. (DOI 10.1023/B:JOMC.0000033252.59423.6b), see eq. A10 in the appendix. I cannot load it on the web due to the copyright. Only one assumption: the moments of order 4 must exist (so, it is not restricted to samples). I do not claim to have discovered the sharp lower bound of the kurtosis, even in its more general form, and I do not care if my 2013 paper is not cited. However I was the first to mention the inequality on the Wikipedia page, and at first glance my own proof seems to be original. I just say that the reader should be directed to a proof valid in all cases, e.g. via a valid source. If the ref works only for samples, the text should be updated accordingly. To conclude, I give you the hint for the full proof for random variables (for vectors, see the 2004 paper), available to anybody aware of math expectations: X1 and X2 are random variables, translate X2, calculate the translation minimizing the variance of the squared difference of the random variables, and look at the expression of the minimized variance: it should be a non negative quantity, hence the desired inequality. Mailto: petitjean.chiral@gmail.com (preferred) or michel.petitjean@univ-paris-diderot.fr 81.194.29.18 (talk) 13:55, 10 December 2014 (UTC) — Preceding unsigned comment added by 81.194.29.18 (talk) 18:54, 8 December 2014 (UTC)
 * Requested further details follow.

 * Padlock-silver-open.svg Not done: According to the page's protection level and your user rights, you should be able to edit the page yourself. If you seem to be unable to, please reopen the request with further details. Anupmehra  - Let's talk!  13:19, 9 December 2014 (UTC)

I pasted the wrong doi of ref.4 (mouse catched the doi of the line above). In fact the Editorial is appended to the paper of Shohat. I cancel my request. Please accept my apologies for the inconvenience caused. Thanks for your patience.81.194.29.18 (talk) 14:33, 10 December 2014 (UTC)

Why kurtosis should not be interpreted as "peakedness"
It seems that people keep wanting to insert something about "peakedness" into the interpretation of kurtosis.

What follows is a clear explanation of why “peakedness” is simply wrong as a descriptor of kurtosis.

Suppose someone tells you that they have calculated negative excess kurtosis either from data or from a probability distribution function (pdf). According to the “peakedness” dogma (started unfortunately by Pearson in 1905, and carried forward by R.A. Fisher through the 14th edition of his classic text, Statistical Methods for Research Workers), you are supposed to conclude that the distribution is “flat-topped” when graphed. But this is obviously false in general. For one example, the beta distribution beta(.5,1) has an infinite peak and has negative excess kurtosis. For another example, the 0.5*N(0, 1) + 0.5*N(4,1) mixture distribution is bimodal (wavy); not flat at all, and also has negative excess kurtosis. These are just two examples out of an infinite number of other non-flat-topped distributions having negative excess kurtosis.

Yes, the continuous uniform distribution U(0,1) is flat-topped and has negative excess kurtosis. But obviously, a single example does not prove the general case. If that were so, we could say, based on the beta(.5,1) distribution, that negative excess kurtosis implies that the pdf is "infinitely pointy." We could also say, based on the 0.5*N(0, 1) + 0.5*N(4,1) distribution, that negative excess kurtosis implies that the pdf is "wavy." It’s like saying, “well, I know all bears are mammals, so it must be the case that all mammals are bears.”

Now suppose someone tells you that they have calculated positive excess kurtosis from either data or a pdf. According to the “peakedness” dogma (again, started by Pearson in 1905), you are supposed to conclude that the distribution is “peaked” or “pointy” when graphed. But this is also obviously false in general. For example, take a U(0,1) distribution and mix it with a N(0,1000000) distribution, with .00001 mixing probability on the normal. The resulting distribution, when graphed, appears perfectly flat at its peak, but has very high kurtosis.

You can play the same game with any distribution other than U(0,1). If you take a distribution with any shape peak whatsoever, then mix it with a much wider distribution like N(0,1000000), with small mixing probability, you will get a pdf with the same shape of peak (flat, bimodal, trimodal, sinusoidal, whatever) as the original, but with high kurtosis.

And yes, the Laplace distribution has positive excess kurtosis and is pointy. But you can have any shape of the peak whatsoever and have positive excess kurtosis. So the bear/mammal analogy applies again.

One thing that can be said about cases where the data exhibit high kurtosis is that when you draw the histogram, the peak will occupy a narrow vertical strip of the graph. The reason this happens is that there will be a very small proportion of outliers (call them “rare extreme observations” if you do not like the term “outliers”) that occupy most of the horizontal scale, leading to an appearance of the histogram that some have characterized as “peaked” or “concentrated toward the mean.”

But the outliers do not determine the shape of the peak. When you zoom in on the bulk of the data, which is, after all, what is most commonly observed, you can have any shape whatsoever – pointy, inverted U, flat, sinusoidal, bimodal, trimodal, etc.

So, given that someone tells you that there is high kurtosis, all you can legitimately infer, in the absence of any other information, is that there are rare, extreme data points (or potentially observable data points). Other than the rare, extreme data points, you have no idea whatsoever as to what is the shape of the peak without actually drawing the histogram (or pdf), and zooming in on the location of the majority of the (potential) data points.

And given that someone tells you that there is negative excess kurtosis, all you can legitimately infer, in the absence of any other information, is that the outlier characteristic of the data (or pdf) is less extreme than that of a normal distribution. But you will have no idea whatsoever as to what is the shape of the peak, without actually drawing the histogram (or pdf).

The logic for why the kurtosis statistic measures outliers (rare, extreme observations in the case of data; potential rare, extreme observations in the case of a pdf) rather than the peak is actually quite simple. Kurtosis is the average (or expected value in the case of the pdf) of the Z-scores ($$ Z $$-values), each taken to the 4th power. In the case where there are (potential) outliers, there will be some extremely large $$ Z^4 $$ values, giving a high kurtosis. If there are less outliers than, say, predicted by a normal pdf, then the most extreme $$ Z^4 $$ values will not be particularly large, giving smaller kurtosis.

What of the peak? Well, near the peak, the $$ Z^4 $$ values are extremely small and contribute very little to their overall average (which again, is the kurtosis). That is why kurtosis tells you virtually nothing about the shape of the peak. I give mathematical bounds on the contribution of the data near the peak to the kurtosis measure in the following article:

Kurtosis as Peakedness, 1905 – 2014. R.I.P. The American Statistician, 68, 191–195.

I hope this helps.

Peter Westfall

P.S. The height of the peak is also unrelated to kurtosis; see Kaplansky, I. (1945), “A Common Error Concerning Kurtosis,” Journal of the American Statistical Association, 40, 259. But the “height” misinterpretation also seems to persist.

P.P.S. Some believe that the "peakedness" and "flatness" interpretation holds in the special case of symmetric unimodal distributions, because in the esteemed journal Psychological Methods, De Carlo (1997) states at the beginning of the abstract to his paper "On the Meaning and Use of Kurtosis," as follows:

“For symmetric unimodal distributions, positive kurtosis indicates heavy tails and peakedness relative to the normal distribution, whereas negative kurtosis indicates light tails and flatness.”

But this statement is also easily shown to be false as regards "peakedness" and "flatness":

Take a U(-1,1) mixed with a N(0,1000000), with mixing p=.0001 on the normal. The distribution is symmetric and unimodal, has extremely high kurtosis, but appears flat at its peak when graphed. The high kurtosis here, as with all distributions, is explained by potential outliers, not by the peak.

Now mix a beta(.5,1) with a -beta(.5,1), with equal probabilities. The distribution is symmetric and unimodal, has negative excess kurtosis, but has an infinite peak. The negative excess kurtosis here, as with all distributions, is explained by paucity of potential outliers, not by the peak. (The maximum possible absolute $$ Z $$ is around 2.24 for this distribution).

— Preceding unsigned comment added by 129.118.195.172 (talk) 16:07, 31 August 2017 (UTC)

 * Peakedness (as used in practical applications) is not supposed to imply anything about the statistical distribution. Higher kurtosis suggest more/higher peaks in an associated data series (e.g. a time series). Therefore, it does not help to argue about the shape of the distribution when you discuss peakedness. It is not intended to be interpreted that way (though a lot of people believe that). See the discussion above about Peakedness. 79.136.121.89 (talk) 08:41, 20 September 2023 (UTC)

Are the terms leptokurtic/platykurtic still meaningful?
If kurtosis is not a measure of the "peakedness", are the terms "leptokurtic" and "platykurtic" still meaningful? Don't they just mean "more peaked" and "less peaked"? Or do they need to be either abandoned or re-defined? --Roland (talk) 21:41, 5 November 2018 (UTC)

 * Pearson really botched this with with his poor word choices. How about "magnacaudatic" instead of "leptokurtic"; "parvacaudatic" instead of "platykurtic"; "mediacaudatic" instead of "mesokurtic"? Those are Latin-ish terms for "heavy-tailed", "light-tailed", and "medium-tailed." Or, just use the latter terms and eschew obfuscation. BigBendRegion (talk) 13:19, 6 November 2018 (UTC)
 * It looks, not only should the terms "leptokurtic", "platykurtic" and "mesokurtic" cease to be used, but also the term "kurtosis" per se should retire, as it just means "peakedness". Isn't it time to invent a Greek-based term meaning "tailedness"? The Greek word for "tail" is "ουρά", or "oura". What would be an Anglocized word based on that to mean "tailedness"? The word "kurtosis", if continues to be used, will be a misnomer, or anachronism. --Roland (talk) 20:58, 8 November 2018 (UTC)

Excess Kurtosis as a Measure of Agreement of 2 Datasets
When comparing 2 datasets, we often compute the distribution of the differences, or errors, of the 2 datasets. When excess kurtosis was considered a measure of "peakedness", a larger positive excess kurtosis would imply better agreement. Now it has been realized that excess kurtosis is actually a measure of "tailedness". What, then, should we wish for when we wish 2 datasets agree? A kurtosis as small as possible? --Roland (talk) 00:59, 27 November 2018 (UTC)

 * Please give a cite. I have heard of using quantile matching to measure agreement between data sets, but never kurtosis. In any event, similar kurtosis of two data sets indicates similar tail weight (or outlier character) for the two, so in that sense there is a match of sorts. BigBendRegion (talk) 13:43, 28 November 2018 (UTC)

 * I have a copy of Numerical Recipes in FORTRAN: The Art of Scientific Computing by Press et al., 2nd Ed., 1992. A subroutine, moment, in this book gives you the mean, average deviation, standard deviation, skewness and excess kurtosis the same time. (The explanation of excess kurtosis in the book is based on the old idea of "peakedness".)

 * I often compare model results with observed data that were acquired at different times and locations. One thing I do is computing the differences between the model results and the observed data and examining the statistics of the differences. Naturally, the modelers aim to make their results agree with the observed data as much as possible, and they would look at every such statistic as a measure of level of agreement: smaller absolute values of the mean, the standard deviation and the skewness suggest better agreement than larger ones.

 * As for the excess kurtosis, if it were a measure of "peakedness", a larger positive one would be better than a smaller one. However, now it is a measure of "tailedness", and a larger tailedness, or more outliers, would imply worse agreement. This seems to suggest that we now should aim to make excess kurtosis as small as possible rather than as large as possible. --Roland (talk) 21:24, 3 December 2018 (UTC)

Sole reference in section "Kurtosis convergence" is a dead link
The section's reference, http://www.cs.albany.edu/~lsw/homepage/PUBLICATIONS_files/ICCP.pdf, is dead.

Kurtosis in SPSS
It may also be mentioned that $$G_2$$ is used by SPSS to calculate the kurtosis. — Preceding unsigned comment added by Ad van der Ven (talk • contribs) 08:54, 25 April 2019 (UTC)

title: Talk:Kurtosis


text:
Neil Hannon

Edward Neil Anthony Hannon (born 7 November 1970) is a singer and songwriter from Northern Ireland. He is the founder and frontman of the chamber pop group the Divine Comedy, and is the band's only constant member since its inception in 1989. Hannon wrote the theme tunes for the television sitcoms Father Ted and The IT Crowd, as well as the original songs for the musical film Wonka (2023).

Early life and education
Hannon was born in Derry, Northern Ireland, the son of Brian Hannon, a Church of Ireland minister in the Diocese of Derry and Raphoe and later Bishop of Clogher. He spent some of his youth in Fivemiletown before moving with his family to Enniskillen, in County Fermanagh, in 1982. While there, he attended Portora Royal School.

Hannon enjoyed synthesizer-based music as a youngster; he has identified the Human League and Orchestral Manoeuvres in the Dark (OMD) as "the first music that really excited [him]". In the late 1980s he developed a fondness of the electric guitar, becoming an "indie kid".

Career
Hannon is founder and mainstay of the Divine Comedy, a band which achieved their biggest commercial success in the mid- to late-1990s with the albums Casanova (1996), A Short Album About Love (1997), and Fin de Siècle (1998). Hannon continues to release albums under the Divine Comedy name, the most recent being Office Politics (2019). In 2000, he and Joby Talbot contributed four tracks to Ute Lemper's collaboration album, Punishing Kiss.

Hannon composed the theme music for the sitcoms Father Ted and The IT Crowd, the former theme composed for the show and later reworked into "Songs of Love", a track on the Divine Comedy's breakthrough album Casanova. Both shows were created or co-created by Graham Linehan. For the Father Ted episode, "A Song for Europe", Hannon co-wrote and sang "My Lovely Horse", the song Ted and Dougal enter in Eurosong (a parody of the Eurovision Song Contest). For the same episode, Hannon wrote "The Miracle Is Mine", the 'typical' Eurovision ballad sung by Ted's nemesis, Father Dick Byrne. A dream sequence in the episode shows Ted and Dougal in the song's pop video, with Hannon providing vocals. Hannon also wrote and performed "My Lovely Mayo Mammy", sung by Eoin McLove in the episode "Night of the Nearly Dead", and wrote "Big Men in Frocks", sung by Niamh Connolly (played by Clare Grogan) in "Rock-a-Hula Ted". When a raffle is held in order to raise funds to repair the roof of the parochial house, the Kraftwerk-esque quartet of priests enlisted to perform play an electronic piece of music composed and performed by him. Both of the advertisements for telephone numbers; in The IT Crowd (the new emergency number) and Father Ted (Priest Chatback) have jingles composed by Hannon. In the episode "A Christmassy Ted", his name is mentioned by Mrs Doyle while she attempts to guess that of the mysterious guest.

In 2004, Hannon performed alongside the Ulster Orchestra for the opening event of the Belfast Festival at Queen's. In 2005, he contributed vocals to Talbot's soundtrack for the movie version of The Hitchhiker's Guide to the Galaxy.

In 2006, it was announced that Hannon was to lend vocals to the Doctor Who soundtrack CD release, recording two songs – "Love Don't Roam", for the 2006 Christmas special "The Runaway Bride"; and a new version of "Song for Ten", which originally appeared in 2005's "The Christmas Invasion". On 12 January 2007, The Guardian website's "Media Monkey" diary column reported that Doctor Who fans from the discussion forum on the fan website Outpost Gallifrey were attempting to organise mass downloads of the Hannon-sung "Love Don't Roam", which was available as a single release in the UK iTunes Store. This was in order to attempt to exploit the new UK Singles Chart download rules, and get the song featured in the Top 40 releases.

The same year, Hannon sang and wrote the lyrics for the song "Somewhere Between Waking and Sleeping" on the Air album Pocket Symphony, released in the United States on 6 March 2007. This song had been originally written for and sung by Charlotte Gainsbourg on her 2006 album 5:55. Though it was not included on its 2006 European release, it was added as a bonus track to its American release on 24 April 2007.

Hannon won the 2007 Choice Music Prize for his 2006 album, Victory for the Comic Muse. It was announced the following day that he had left EMI by 'mutual consent'. When the band Keane played at the O2 Arena in London that July, "A Bad Dream" was introduced by Hannon, who read the poem "An Irish Airman Foresees His Death" by W. B. Yeats, upon which the song is based.

In 2009, Hannon collaborated with Thomas Walsh from the Irish band Pugwash to create a cricket-themed pop album, under the name the Duckworth Lewis Method. The first single, "The Age of Revolution", was released in June 2009, and was followed by an eponymous full-length album the week after. The group's second album, Sticky Wickets, came out in 2013.

Hannon composed the music for a stage adaptation of Arthur Ransome's novel Swallows and Amazons (1930), which premiered in December 2010 at the Bristol Old Vic, with book and lyrics by Helen Edmundson. A new Divine Comedy album, Bang Goes the Knighthood, was released in May 2010.

On 20 April 2012, Hannon's first opera commission, Sevastopol, was performed by the Royal Opera House in London. The piece was part of a program called OperaShots, which invites musicians not typically working within the opera medium to create an opera, and was based upon Leo Tolstoy's Sevastopol Sketches. Hannon's second opera for which he composed the music, In May (with book by Frank Alva Buecheler and English translations by Tim Clarke), premiered at Lancaster University's Nuffield Theatre in May 2013.

In 2013, Hannon was commissioned by the Southbank Centre to compose a piece for the Royal Festival Hall's refurbished organ: To Our Fathers in Distress, "a kind of oratorio" for chorus, strings and organ, premiered at the Hall in London on 22 March 2014, as part of the Pull Out All the Stop Festival, and was inspired by Hannon's father, the Rt Rev Brian Hannon, who had suffered from Alzheimer's disease before his death in 2022. In 2015, Neil won the 2015 Legend Award from the Oh Yeah organisation in Belfast.

Hannon wrote the songs for the 2023 musical film Wonka.

On 28 January 2024 Hannon was the guest of Michael Berkeley on the BBC Radio 3 programme Private Passions. His choices included Chopin's Nocturne in E flat major, Op 9, No. 2, Ravel's String Quartet in F major and "Montague Terrace (In Blue)" by Scott Walker.

Personal life
Since 2009 (wed Jan 2023), Hannon's partner has been Irish musician Cathy Davey. The couple live in County Kildare. He was previously married to Orla Little, with whom he has a daughter, born about 2002. With Davey, Hannon is a patron of the Irish animal charity My Lovely Horse Rescue, named after the Father Ted Eurovision song for which he wrote the music.

Politically, Hannon describes himself as being "a thoroughly leftie, Guardian-reading chap, but of the champagne socialist variety".

The Divine Comedy

 * Fanfare for the Comic Muse (1990)
 * Liberation (1993)
 * Promenade (1994)
 * Casanova (1996)
 * A Short Album About Love (1997)
 * Fin de Siècle (1998)
 * Regeneration (2001)
 * Absent Friends (2004)
 * Victory for the Comic Muse (2006)
 * Bang Goes the Knighthood (2010)
 * Foreverland (2016)
 * Office Politics (2019)

Other contributions

 * The Cake Sale (compilation) – "Aliens"
 * Doctor Who: Original Television Soundtrack (compilation) – "Song for Ten" (performer)
 * Doctor Who: Original Television Soundtrack (compilation) – "Love Don't Roam" (performer)
 * Amélie (compilation) – "Les Jours tristes" (instrumental version) (co-writer)
 * L'Absente by Yann Tiersen – "Les Jours tristes" (English version) (co-writer and performer)
 * The Hitchhiker's Guide to the Galaxy Soundtrack (compilation) – "So Long and Thanks for All the Fish" (performer)
 * Reload by Tom Jones – "All Mine" (as the Divine Comedy) (performer)
 * Pocket Symphony by Air – "Somewhere Between Waking and Sleeping" (writer and performer)
 * Songs from the Deep Forest by Duke Special – "Our Love Goes Deeper Than This" (performer)
 * Hyacinths and Thistles by the 6ths – "The Dead Only Quickly" (singer)
 * Eleven Modern Antiquities by Pugwash – "Take Me Away" (performer)
 * Punishing Kiss by Ute Lemper – (multiple tracks) (performer and writer)
 * Les piqûres d'araignée by Vincent Delerm – "Favourite Song" (duet track)
 * A Mãe by Rodrigo Leão – "Cathy" (performer)
 * God Help the Girl by God Help the Girl – "Perfection as a Hipster"; Neil Hannon with Catherine Ireton (performer)
 * "No Regrets" by Robbie Williams – backing vocals with Neil Tennant of Pet Shop Boys
 * The Silent World of Hector Mann by Duke Special – "Wanda, Darling of the Jockey Club"
 * Irrepetible by Coque Malla – duets on "My Beautiful Monster"
 * Adventure Man by Eg – "Pay Later" (co-writer and performer)
 * Adventure Man by Eg – "If You Run" (co-writer and performer)
 * Wonka – songs (co-writer)

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Neil Hannon


text:
Talk:The Hundred and One Dalmatians

Rewrite
The old Wikipedia problem. I look for information on something and just find a stub. I've expanded the article a lot, especially by adding details of the plot. However, it has been a long time since I read the book and I may have got some details wrong. Any corrections would be welcome. mh. 20:57, 22 July 2006 (UTC)

she wrote a sequel too, who name temporarily escapes me ... something like "The twilight barking" ... -- Tarquin 11:03 Feb 6, 2003 (UTC)

 * I've found a reference to The Starlight Barking (1967) - would that be it?
 * --Paul A 01:56 Feb 7, 2003 (UTC)

 * bingo! I read it when I was a kid. Not as good as the first one. -- Tarquin 10:40 Feb 7, 2003 (UTC)

Yes, a lot is wrong
It's Pongo and Missus Pongo. And it's "Twilight Barking."

Nota bene the spelling. — Preceding unsigned comment added by 74.96.231.142 (talk) 10:20, 21 March 2012 (UTC)

Basically the first thing that needs to be done here is a basic round of proofreading. "Would", but I ran into the semi-protection and I'm loath to go through with the bother of creating an account, remembering the login details, and logging on every time I want to fix something on a site that otherwise allows no-log-in editing :p 193.63.174.208 (talk) 10:05, 15 June 2012 (UTC)

Location of Hell Hall?
Just out of curiosity I'm trying to work out where Hell Hall was actually set, and the route they took to get there. The book places it in the village of Dympling in Suffolk but Multimap and MS Map Point have no details of anywhere called Dympling, and Google just comes up with recipes for dumplings. However there is a place called Shimpling which seems to fit the bill. Combining details in the book with the route into London in The Starlight Barking suggests this:

 * 1) Regent's Park/Outer Circle
 * 2) Camden Town
 * 3) Outskirts of London
 * 4) Epping Forest (stopped overnight at a village here)
 * 5) cross country (route avoiding roads saves miles travelling into Essex, so it probably misses out the less direct route along the old A11)
 * 6) small hamlet with rundown hall
 * 7) Bridge across the River Stour
 * 8) Sudbury
 * 9) "Dympling"

101 Dalmatians suggests that they planned to follow the main road from London to Suffolk. This was before the M11 was built, so it looks like the route was intended to be A11/A104/B1393/A414/A1184 (all used to be the A11) and then somehow getting from Bishop's Stortford to Sudbury.

As far as the hall goes, there are a few in the area near Shimpling. These include:
 * Kentwell Hall
 * Melford Hall
 * Nether Hall
 * Blacklands Hall

I'm not sure which of these (if any) is most likely. According to the description in the book it was 200 years old (in the 1950s) with a similar hall next door (the farmhouse in "Dympling"). It has a large perimeter wall with a folly, and a large pond in the grounds, and looks rather like a face. Of course it could be that the hall doesn't exist as this is fiction after all.

The return route is:
 * 1) "Dympling"
 * 2) Gypsy camp
 * 3) village with bakery (described as 5 miles out of "Dympling")
 * 4) country church
 * 5) village where bakery burns down (5 miles from first village)
 * 6) picked up by removal van (with Staffordshire Terrier)
 * 7) presumably driven back to London along the old A11
 * 8) St John's Wood
 * 9) Regent's Park/Outer Circle

The distance from Shimpling to Regent's Park is about 55 miles, which is consistent with the sheepdog's plan to travel about 5 miles a day over 10 - 12 days.

This is all speculation, but it would be interesting to flesh out some of the details in the book. It does seem odd that Smith gives detailed descriptions of London but picks a fictitious name for the village in Suffolk.

mh. 21:29, 27 November 2006 (UTC)

 * Not necessarily, a similar case is the film The Maggie, which has real placenames at first, but switches to fictional placenames once they get through the Crinan Canal. PatGallacher (talk) 20:15, 24 October 2009 (UTC)

 * Responding to myself, according to the "smart little house" in Regent's Park is 1 St Andrew's Place and is used by the Faculty of Pharmaceutical Medicine. Also, from what I remember, the 1st edition of 101 Dalmatians has a much better map of the route than the 2006 Egremont edition that I have here. mh. 23:53, 27 November 2006 (UTC)

As "Novels by Dodie Smith" is a sub-cat of "English novels" I propose to revert back unless anyone objects. PatGallacher (talk) 20:15, 24 October 2009 (UTC)

Pending changes
This article is one of a number selected for the early stage of the trial of the Pending Changes system on the English language Wikipedia. All the articles listed at Pending changes/Queue  are being considered for level 1 pending changes protection.

The following request appears on that page:

Comments on the suitability of theis page for "Pending changes" would be appreciated.

Please update the Queue page as appropriate.

Note that I am not involved in this project any much more than any other editor, just posting these notes since it is quite a big change, potentially

Regards, Rich Farmbrough, 00:20, 17 June 2010 (UTC).

Radio drama
I found on Youtube a radio adaptation of this work. I'm not sure when it was made -- before or after the movie, or what. I don't know anything about it really, but it definitely and I would think it deserves mention here... Bobnorwal (talk) 23:07, 13 June 2011 (UTC)

Semi-protected edit request on 3 February 2018
ADD: It was published in the United States in serial form in Woman's Day, June through September, 1956, as The Great Dog Robbery. Bonitakale (talk) 13:41, 3 February 2018 (UTC)
 * Red information icon with gradient background.svg Not done: please provide reliable sources that support the change you want to be made. Eggishorn (talk) (contrib) 18:45, 3 February 2018 (UTC)

Location needed?
"The 1991 reissue was the twentieth highest earning film of the year for domestic earnings."

Where was this? Presumably the US market? — Preceding unsigned comment added by 51.9.7.3 (talk) 12:55, 2 August 2020 (UTC)

title: Talk:The Hundred and One Dalmatians


text:
Male dominance (BDSM)

Male dominance, or maledom is a BDSM practice where the dominant partner is male. A sexually dominant male in BDSM practices is also known as a maledom. Maledoms can be professional as well as non-professional. The term ProDom is used for a professional male dominant who earns money by working as a professional dominant as part of the sex industry. A maledom who role-plays a paternal figure is also known as Daddy Dom.

Practices of domination common to many BDSM and other various sexual relationships are also prevalent, such as various forms of body worship including cock and ball worship, ass worship and foot worship, fellatio, tease and denial, corporal punishment including spanking, caning and whipping, breast torture, pussy torture, orgasm denial, and as well as verbal humiliation, face slapping, hair pulling, wax play, spitting, golden showers, forced orgasm, "forced" chastity, and irrumatio. Male dominance may also take place in the form of clothed male, naked female.

Male dominants are often referred to as dom, master, owner, sir, taskmaster, corporalist, boss or top. For some people, male dominance is only used in sexual and intimate scenarios, but for others male dominance can be included in 24/7 BDSM relationships.

A 1995 study indicated that 71% of heterosexual males preferred a dominant-initiator role, but a more recent study from 2015 indicates that 29.5% of men who are active in BDSM express a preference for a dominant role, 24% consider themselves to be switches and 46.6% of men prefer the submissive role. Another survey from 2017, challenges these conclusions, and indicates that men tend to self-identify as Dominant, Master, Top, or Sadist (DMTS) and always perform dominant roles.

Popular culture

Maledom scenarios are common in BDSM fiction, including works such as the Story of O and the works of John Norman and Adrian Hunter. Maledom is a growing adult film genre.

Maledom fiction began with the works of the Marquis de Sade who wrote about sexual scenarios in which men tortured others, primarily women. The term sadism is derived from de Sade's name. Since then, the lifestyle around male dominance has grown into a large part of the BDSM scene.

The Fifty Shades novel series by E. L. James is associated with the cultural mainstreaming and normalising of BDSM. The books achieved great commercial success, with the first volume selling over 100 million copies worldwide.

Other works include:
 * John Warren, The Loving Dominant, Greenery Press, 2001, ISBN 1-890159-20-4
 * Jack Rinella, The Master's Manual: Handbook of Erotic Dominance, Daedalus Publishing, 1997, ISBN 1-881943-03-8

title: Male dominance (BDSM)


text:
Talk:Alan Freed

Biography assessment rating comment
The article may be improved by following the WikiProject Biography 11 easy steps to producing at least a B article. -- KenWalker | Talk 08:50, 1 July 2007 (UTC)

"Rock and Roll" and "Moondog"
Who did coin the phrase "Rock and Roll"? PMelvilleAustin 11:37 Feb 6, 2003 (UTC)

I've heard that there is a connection between Freed's "Moondog Coronation Ball" and the musician Moondog. Does anyone know more about this? McGravin 00:00 Mar 3, 2004

Yes this correct. Freed used the name Moondog after he used a piece of music called Moondog Symphony by the street musician Moondog as his repeated opening music for his radio show. Moondog subsequently sued Freed on grounds that he was stealing his name. Since Freed was no longer allowed to use the term Moondog he needed a new catch phrase. After a night of heavy drinking he and his friends came up with the name "The Rock and Roll Party" since he was already using the phrase "Rock and Roll Session" to describe the music he was playing on his radio show.

It should also be noted that it was in fact Freed to coin the phrase "Rock and Roll" to describe a form of music. It is true that the phrase appeared in many songs before Freed but this did not describe a type of music. Freed is also credited with popularizing the phrase through his show "The Rock and Roll Party". --Manik52 08:15, 22 April 2006 (UTC)

This is factually wrong. The phrase "Rock and Roll" per se referring to loud, beat-driven music, dates at least as far back as the 1940's. Cf. Billboard magazine (I don't have specific citations in front of me). Etomology (talk) 19:20, 3 February 2021 (UTC)

Billboard magazine, June 22, 1946 pg. 33

https://books.google.com/books?id=9hkEAAAAMBAJ&pg=PT32&dq=%22Rock+and+roll+music%22&hl=en&sa=X&ved=2ahUKEwjSlfexwc7uAhWmVzABHYeyDfwQ6AEwA3oECAcQAg#v=onepage&q=%22Rock%20and%20roll%20music%22&f=false Etomology (talk) 20:06, 3 February 2021 (UTC)

 * Good point and a useful source; OK, have revised the content, mentioning this 1946 use of rock and roll. Note: Alan Freed did not coin the phrase rock and roll; however, by way of his radio show, he popularized it and redefined it. Once slang for sex, it came to mean a new form of music. This music had been around for several years, but… https://www.britannica.com/biography/Alan-Freed Peter K Burian (talk) 22:54, 3 February 2021 (UTC)

Very POV
This needs lots of work as it is very pro-Freed POV and not at all objective (even though I very much agree with a majority of the points made). I'm not going to "tag" it yet, but it certainly needs lots more work to come up to Wikipedia standards — as currently written it is just a defense of Freed and not an encyclopedia article. Rlquall 00:20, 20 May 2005 (UTC)
 * I tend to agree.
 * I've been with R&R at least since Alan came up to the desert, and before; and I don't think the whole story is told here.
 * What about the bit with Morris Levy, the Music Mafia dude? That's where he really got ripped off.
 * They told me that they only made $10.00 out of all that; even the Jews themselves spoke out against Morris Levy, he was a real schmuck.
 * And I don't think Clark faired that well either, as I remember. He really got stuck when they literally forbade him to own his own stuff; he got around it, though, with some kind of by-proxy ownership thing or something.
 * And then there was Peter Tripp. They really socked it to him, but like Freed and Clark, he moved out to California where "miraculously" Rock & Roll was reborn.
 * As for as the name "Moondog"; he was sued back in 1953 for stealing that name from some blind dude: Louis "Moondog" Hardin who was a street poet who had been going by that name for at least 10 years. Alan apologized and changed the name to "rock & roll", but Louis has never been properly credited for his accomplishments, including being the true father of "Moondog Music" as Alan had at first called it.
 * WB2 06:18, 27 May 2005 (UTC)

Was Alan Freed an anarchist? In what sense? Positive or pejorative? I've never heard anything about Alan that suggested to me that he was an anarchist. HUAC types might feel that way about it. But if Alan had any politics (other than play what the kids want to hear and make a buck at it), that's certainly been kept on the down-low. Motives were a lot simpler back in those days. Well, ok -- simpler. -- Anonymous user

I deleted him from category "anarchists"... better to keep that for self-described ones 02:55, 8 February 2006 (Moscow time)

It should be tagged. It's not a bad article, but it's packed with evaluative comments unsupported by references - "great," "legendary," even "smarmy."KD Tries Again (talk) 03:48, 17 July 2009 (UTC)KD Tries Again

There are clips on youtube labeled Alan Freed Show WNEW-TV, New York which would be Channel 5 in New York at that time and aired around 3:00 or 4:00 in the after noon so kids home from school would see the show. Thats the show I recall seeing. In sponsoring Coke a Cola Allen would ask a dancer Candy to take a sip. You saw her pony tail and say something good about the soda and walk off. I met Candy years later in my High School cafeteria. Good work Allen love Ya. Teslafieldmachine (talk) 18:09, 15 May 2011 (UTC)

Current location of interment
Needs to be updated with the current location of the interment of his ashes. See RRHoF press release dated 2002-03-22. Additional links to press articles about the move of the ashes can be found toward the bottom of this web page.

song-writer under...
the Anthony September pseudonym. Stephan KOENIG.

Citations & References
See Footnotes for an explanation of how to generate footnotes using the  tags Nhl4hamilton (talk) 04:55, 1 February 2008 (UTC)

Suggestion for disambiguation note
A disambiguation page should be considered for the topic of "Alan Freed" because there is another, more recent, yet unrelated, Alan Freed in national broadcasting.

See: Beat Radio and XM BPM —Preceding unsigned comment added by 69.235.201.241 (talk) 01:50, 24 September 2010 (UTC)

 * Hi, I am probably the Alan Freed whom 69.235.201.241 referenced almost five years ago. I see there hasn't been any response to that entry. Can the suggestion be discussed and considered by Wikipedians watching this article?

 * Like the Alan Freed whom the article is about, I also work in radio/media. Having the same name and occupation causes issues with identity and search engine results. And this can cause confusion on Wikipedia. I am not related in any way to the earlier Alan Freed.

 * There is no Wikipedia article about me but there are articles about Beat Radio and SiriusXM's BPM in which I am cited. Certainly, I'm not as well-known as the "other" AF; it is not uncommon, however, because of the same name and occupation, that I am confused with him or his son (Alan Freed, Jr.), and, regardless of any confusion, search engines make me practically unfindable because results for the 1950s Alan Freed are more numerous. Further, a third Alan Freed was known for his years in Pittsburgh radio.

 * A disambiguation page should resolve any confusion. As I don't have a Wikipedia account, I will monitor this talk page for activity.

 * Thanks for any consideration.
 * Alan Freed, alive and well
 * Los Angeles
 * 107.207.188.94 (talk) 02:16, 26 June 2015 (UTC)
 * If you are, in fact, Alan Freed, your notability needs to be established according WP:BIO with reliable sources (see WP:RS). If the article is created, I suggest that you avoid editing it yourself (see WP:COI). Sundayclose (talk) 03:05, 26 June 2015 (UTC)

 * Sundayclose, thanks for your response. The suggestion and request are for a disambiguation page.
 * Alan Freed — Preceding unsigned comment added by 2605:E000:6044:4E00:303F:2712:CEF1:D2C8 (talk) 02:44, 28 June 2015 (UTC)
 * That doesn't change anything. Your notability still needs to be well sourced. Everyone doesn't get his name on a disambiguation page simply because he requests. Sundayclose (talk) 02:17, 1 July 2015 (UTC)

 * Yes, Sundayclose, that simply requesting doesn't get is a given. I have presented my case and I think it's a reasonable consideration given the facts. That another Wiki user took the initiative to suggest it five years ago says that I'm not the only person who has had the thought. Thanks for your attention.
 * Alan
 * 2605:E000:6044:4E00:D57A:7CB4:7E78:7CF2 (talk) 05:32, 1 July 2015 (UTC)
 * I don't mean to be harsh, but you keep saying the same thing over and over without providing what is needed. You have only made a request. You have not provided any reliable sources that the Alan Freed to whom you refer is notable according to WP:BIO. There may be a dozen Alan Freeds in the world, but all of them don't belong on a disambiguation page. And it doesn't matter that another person suggested it, or if a dozen people suggest it. Wikipedia operates according to verifiability, not what a couple of people think. You can keep requesting from now until the cows come home, but it won't happen until we see those sources. You may very well be notable enough to be included on a disambiguation page, but your case so far is completely unimpressive. Please click the links I have provided here and above and read them. If you're especially lucky, someone may come along who has nothing better to do than to find the sources if they exist, but since this is not a high traffic article that is very doubtful. There are many more important things to do on Wikipedia. Otherwise, you are wasting our time and your time. And, no offense, but your refusal to get the point is not something I intend to waste any more time on, so this is my last comment here until sources are provided. Best wishes. Sundayclose (talk) 14:34, 1 July 2015 (UTC)

 * There are a some mentions of the Alan Freed of XM BPM at our article on Beat Radio (and also at BPM (Sirius XM)). A simple solution might be a hatnote in this article, along the lines of:  For the radio program director, see Beat Radio.   I'm not saying that's necessary, just that it may avoid any (largely theoretical, I think) confusion and resolve the issue.  Ghmyrtle (talk) 14:47, 1 July 2015 (UTC)
 * Thanks for your comment, Ghmyrtle. Unfortunately, the comments about Freed in both of those articles are entirely unsourced, so it again fails verification. If someone can find appropriate sources for those articles, your suggestion might have some merit. I personally don't consider every reference to a radio personality in an article worthy of mention on a disambiguation page. There are probably several thousands of such people mentioned throughout Wikipedia. But I won't split hairs. We do, however, need sourcing for this Alan Freed somewhere on Wikipedia. Sundayclose (talk) 15:27, 1 July 2015 (UTC)
 * Sources do exist that could be used to expand improve the Beat Radio article -, , ,   - but they're not that strong.  Until someone does that, I tend to agree there's no need for anything to change here.  "Someone... who has nothing better to do than to find the sources if they exist" =  Ghmyrtle (talk) 15:55, 1 July 2015 (UTC)

 * Sundayclose, I am not a Wikipedian like you and Ghmyrtle. The reason for the suggestion was supported in detail in my initial comment, and generally in the previous comment. I believe it to be a reasonable consideration for the specific reasons outlined (I'd like to believe you understand the basis for the suggestion, but your remark, "I personally don't consider every reference to a radio personality in an article worthy of mention on a disambiguation page," suggests otherwise). I do appreciate your quick responses, but not the tone, as it has become combative and belittling. Your assertion of WP:IDHT here is inappropriate as I have not been "saying the same thing over and over"; my comments were of a clarifying nature, discussion, a process which I understand the Wikipedia community encourages. I can only hope for other, more helpful and respectful Wikipedians to weigh in. Thank you for your extremely valuable time. I apologize/not apologize for wasting it.

 * P.S. In addition to the sources cited by Ghmyrtle, there is a collection of press coverage about Beat Radio and me at beatworld.com/press, and a web search for "Alan Freed XM" delivers results for my affiliation with the satellite service (as well as results for the same-named person from the '50s...).
 * Alan 2605:E000:6044:4E00:D57A:7CB4:7E78:7CF2 (talk) 21:48, 1 July 2015 (UTC)

My apologies if I came across with a tone that you don't appreciate. That said, however, when someone is asked repeatedly to provide information and fails to do so (my assumption is because that person wishes for someone else to do it for them), and especially when that person is presented with several links that clearly and in detail explain how the process works here, but there is no response to the requests, I must assume either that person doesn't understand the request, or doesn't care to understand, or doesn't wish to go to the trouble to provide the information. I won't go so far as to characterize which of those describes you (if any do). I make no apologies for my comments about radio personalities in regard to appropriateness for a disambiguation page; that is not a tone; it is an opinion. Now, I hope my involvement here is resolved, and I'll leave it to other editors to decide how to proceed. Thanks to all. Sundayclose (talk) 22:32, 1 July 2015 (UTC)
 * Ghmyrtle's suggestion of a hatnote sounds like a good idea. A minimal approach that gets the job done. That is actually what I had in mind. (I have purposefully avoided getting too hands-on here, being cognizant of the sensitivity among Wiki editors regarding WP:NPOV and to those having a close connection to articles. While the latter concern doesn't exactly apply in this instance, I have taken a reserved approach in deference to the former and because I am not an experienced Wiki editor. I am, however, happy to contribute.)
 * Alan 2605:E000:6044:4E00:B5C2:C94E:74EA:5852 (talk) 01:46, 3 July 2015 (UTC)
 * I've added a brief hatnote - does that do the job? Ghmyrtle (talk) 08:22, 3 July 2015 (UTC)
 * With respect Ghmyrtle, I reverted. All references to Freed in that article are unsourced. By WP:V standards, its veracity is unknown. I tagged that article. Sundayclose (talk) 14:51, 3 July 2015 (UTC)
 * "I'll leave it to other editors to decide how to proceed. Thanks to all."' Hmm.... Ghmyrtle (talk) 14:52, 3 July 2015 (UTC)
 * I was referring to the process of locating and citing sources, not to violation of Wikipedia's core principle of verifiability. Thanks. Sundayclose (talk) 15:08, 3 July 2015 (UTC)
 * But that's surely a separate issue. We still link to other articles, even if those articles are not very good.  If the Beat Radio article needs to be improved, by adding  reliable sources, that should obviously be done.  Until that time, it remains the best (if not only) source on Wikipedia for information (unsourced information - but still information) on "the other" Alan Freed.    Ghmyrtle (talk) 16:38, 3 July 2015 (UTC)
 * Information about the "other" Alan Freed is subject to deletion in other articles if it does not get sourced properly, especially now that the articles are tagged. By Wikipedia standards any other Alan Freed has not been verified as notable. In the mean time, there is nothing urgent about the hatnote. If Freed gets sourced properly, add the note again. If my name was Alan Freed and I added myself randomly to an article without a source, that doesn't mean my name should be in a hatnote here. Sundayclose (talk) 17:42, 3 July 2015 (UTC)
 * Ghmyrtle, thanks for your help. I would only suggest that a hatnote refer to me as "satellite radio personality/programmer" as my activity was not limited to programming and I am known as much or more for being an on-air host as programming; the article about XM BPM, as mentioned before, details my involvement there. And plenty of verifable data about my pre-XM work is available online (and off): beatworld.com/press makes it easy for anyone so inclined to locate plenty of third-party press reports in one place. (And if someone is suggesting that I have added my name randomly to Wiki articles (and without a source...), they would be incorrect. A more thin-skinned person may consider that a personal attack.) Alan 2605:E000:6044:4E00:61D4:FEBF:65B0:8F6D (talk) 01:46, 4 July 2015 (UTC)

If the hatnote is ever restored, the descriptor should conform to the description of Freed in the sources cited; and since there are currently no sources, that is a moot point. And if someone will give me a direct quotation of my comments in which I said that Alan Freed added his name to a Wikipedia article, I'll ask for a block of myself. Sundayclose (talk) 02:23, 4 July 2015 (UTC)

WJW as opposed to WINS
I find the article a bit confusing. I am sure the Moondog name can from the NY musician but while Freed was on WJW his theme song was "Blues for the Red Boy" by Todd Rhodes (King Records), not the Moondog Symphony. The law suit came after he moved to New York which is not clear at all from the text.

I listened to the WJW show nightly after he got a free plug from another DJ in Cleveland, Bill Randall. Not sure how to go about verifying the theme song issue. — Preceding unsigned comment added by Russbo (talk • contribs) 21:35, 1 November 2011 (UTC)

Heritage?
The article now says "Freed was born to a latin (sic) father...and Welsh-American mother...." I had always been under the impression that he was of Jewish extraction, but even if he wasn't, "Latin" is too broad a descriptor. It needs to be changed. Dozzzzzzzzzing off (talk) 03:26, 13 November 2013 (UTC)

Alan Freed was not half Latin. He self-identified as a Jew. His father was Jewish American (I think originally from Germany before WWII. His mother was Welsh-American.  I knew the family personally in Ohio.NDeWolfe (talk) 16:14, 1 July 2014 (UTC)

 * A quick Google Books search should convince anyone that Freed was half-Jewish through his Russian immigrant father (his mother was Welsh). I cannot find any evidence of Latin descent.   Levdr1 lp  /  talk  19:38, 13 November 2013 (UTC)

 * Thank you. Dozzzzzzzzzing off (talk) 03:19, 14 November 2013 (UTC)

Brooklyn Paramount
Pleeze reformat this section into a table by years. As currently formatted, information is almost impossible to find. Also, what happened with the 1959 Christmas Jubilee? There's only a title. 108.18.136.147 (talk) 14:25, 5 June 2015 (UTC)

bad grammar
"who" is not the appropriate relative pronoun for a store. If you mean Mintz, put this phrase after his name. If you mean the store, talk to a teacher of the English language and find out what relative pronoun to use. 108.18.136.147 (talk) 14:39, 5 June 2015 (UTC)

Semi-protected edit request on 10 September 2015
Change "Laraine Newman played the role of Carole King." to "Laraine Newman played the role of Teenage Louise, a character modeled loosely on Carole King."

Zoetmb (talk) 19:38, 10 September 2015 (UTC)
 * Correct - done. Thank you.   Ghmyrtle (talk) 19:47, 10 September 2015 (UTC)

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title: Talk:Alan Freed