Talk:Sexual dimorphism measures

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"number of individuals of the f sex in a sample of size nf composed of individuals of the f sex"

This is not nonesense, it is very precisely correct. (I'd like to say the following: let's suppose you have nf=5 apples and then you ask for the number of apples among nf=5 apples, is this nonsensical?--Sipina (talk) 17:34, 10 June 2010 (UTC))

The variable n, subscript f, refers simply to "a number".

This number is the total count of the "individuals" in the "sample"

(not, for example, any other metric of those individuals).

n is just a size, a count, an integer.

In fact, n is just the usual pronumeral (character representing a number) for the sample-size.

For convenience, the same conventional pronumeral (n) is also used for the sample-size of the m sex population.

So the subscript (f) describes that it is the sample-size of the f sex population.(This is nonsensical as well: the sample size means the size of a sample, not of a population. As is known a sample is a part of a population, in general, a little part of that population. To clear up all this discussion about nf: you have a sample of size n which is composed of individuals belonging to two sexes; you may count those individuals of one sex, say the f sex, and find that this number is nf; this way, the number of individuals of the other sex, say m, is nm=n-nf --Sipina (talk) 17:59, 10 June 2010 (UTC)

Therefore, we have a sample of the f sex of size n subscript f.

There is some redundancy in the quoted description.

It would be more concise (but perhaps less clear) were any of the following equivalent expressions offered instead.

number of individuals of the f sex in a sample of size nf drawn from that sex

number of individuals in a sample of size nf drawn from the f sex

a sample of size nf drawn from the f population

an f-population sample-size nf

My personal favourite is the last, but people call me incomprehensible when I'm that terse.

They call me long-winded when I go the other way though.

What is important here though is, it is NOT possible to say merely

"number of individuals of the f sex in a sample of size nf"

because then there could be m sex individuals in the sample.

For the same reason,

"number of individuals of the f sex in a sample of size nf composed of individuals"

is also an inadequate description.

It must be specified that the sample is taken from the f population, not m population, nor total population.

Hope that clears things up a bit.

There's a lot of work in this article, but it needs work to make it accessible to non statisticians.

I also felt the most important point was lost in jargon.

Sexual dimorphism exists in ALL sexual creatures, by definition!

Sexual reproduction "super-charged" evolution by randomizing gene exchange between two individuals.

Male sex cells are different to female sex cells, that's what makes it work.

In some metrics in any species the dimorphism is 100%. Sex cells are one example.

Another example is that male monarch butterflies have a particular mark on their wings and females don't.

100% dimorphism on that specific metric, whatever the sample size or population!

Sexual dimorphism is a real and obvious phenomenon.

What is tricky is putting a number on it.

It is clear that some species have radically different biology and behaviour between male and female,

others have very little distinction in physical characteristics or behaviour.

The two big questions are:

1. Can we give each of the sexual species a meaningful score out of 10 for males and females being different?

2. On any given characteristic, how do we measure male - female divergence? Say human height or weight.(This is what the article tried to answer--Sipina (talk) 18:05, 10 June 2010 (UTC))

It'd be nice if someone could do some work to make the mathematical-theoretical issues more accessible.(No problem: ask questions and the answers will try to do their best--Sipina (talk) 18:05, 10 June 2010 (UTC))

It'd be even nicer if someone could report a little on academic interpretation of the significance of some actual field data.

Gotta say I'm awestruck by the work in what's been done already though, thanks! :)