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How Correlated Are You?

allendowney.com

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Re: How Correlated Are You?

#51

Post by HDMI_Cable »

AeiumNE wrote: post #47
HDMI_Cable wrote: post #44

Earlier quoted context omitted.

I'm not sure if female social capital in gorillas is the reason for the shorter penis size. Because in other primates, like bonobos, for example, where females predominate and have lots of choice, penis size is still small. I think the main reason why is because sex is used in humans for pair-bonding, and many human cultures have much more monogamous lifestyles—raising children together for example. Sex in humans is…

I thought it was because apes fight by grabbing and tearing, so with that evolutionary pressure applied you basically end up with the minimum effective protrusion. Also, this is why I plan to avoid fighting apes or at least try to wear some good pants if I do.

You may be right. If there was a selective pressure against large penises due to intra-male fighting, and the lack of a strong enough pressure for larger penises (due to the relative absence of pair-bonding), that would support the observation that gorilla penises are small. For humans, where that sort of fighting doesn't happen, and where pair-bonding is important, penis size would naturally drift upwards until they become too big, or they take too much energy to grow/utilize.

One way to test this would be analyze the fighting techniques of various primate species, and then bin them based on their penis size and relative monogamy. If all primates that grab and tear have small penis, irrespective of their pair-bonding, then perhaps the former is more important.

Re: How Correlated Are You?

#52

Post by crackedbassoon »

marginalia_nu wrote: post #38
ckdot2 wrote: post #37

Earlier quoted context omitted.

How would you consider that? The article states that all the calculations were based on the ANSUR-II dataset. If the values in that dataset were wrong, of course the results of the calculations may not be correct.

That's not really how measurements work though. All real-world measurements have precision associated with them. It's not a matter of measurements being correct or being wrong. As GP hints at, since you don't measure an arm the same way you measure an ear, it's reasonable to expect the errors to have different characteristics.

Yes exactly this. And with noisier measures come lower correlations.

Re: How Correlated Are You?

#53

Post by crackedbassoon »

ckdot2 wrote: post #42
marginalia_nu wrote: post #38

Earlier quoted context omitted.

That's not really how measurements work though. All real-world measurements have precision associated with them. It's not a matter of measurements being correct or being wrong. As GP hints at, since you don't measure an arm the same way you measure an ear, it's reasonable to expect the errors to have different characteristics.

Of course we can expect errors, I don't doubt that. That's what I mean with "wrong". But still, how should the author consider that if he doesn't know about the precision nor quantity of errors? I just don't think that's the topic of the article. The article is about correlations in the dataset, no matter, if the dataset may contain errors.

Intuition tells me it would be harder to achieve a precise measurement of ear protrusion than of a larger body part. This could be wrong of course, but it is generally good scientific practice to think about measurement errors when comparing sets of correlations. This may be especially true for a Bayesian, since Bayesian statistics (when placed in opposition to frequentist) strongly emphasises a deep consideration of all sources of uncertainty.

Re: How Correlated Are You?

#54

Post by pessimizer »

ckdot2 wrote: post #42
marginalia_nu wrote: post #38

Earlier quoted context omitted.

That's not really how measurements work though. All real-world measurements have precision associated with them. It's not a matter of measurements being correct or being wrong. As GP hints at, since you don't measure an arm the same way you measure an ear, it's reasonable to expect the errors to have different characteristics.

Of course we can expect errors, I don't doubt that. That's what I mean with "wrong". But still, how should the author consider that if he doesn't know about the precision nor quantity of errors? I just don't think that's the topic of the article. The article is about correlations in the dataset, no matter, if the dataset may contain errors.

https://en.wikipedia.org/wiki/Significance_arithmetic

> Significance arithmetic is a set of rules (sometimes called significant figure rules) for approximating the propagation of uncertainty in scientific or statistical calculations. These rules can be used to find the appropriate number of significant figures to use to represent the result of a calculation. If a calculation is done without analysis of the uncertainty involved, a result that is written with too many significant figures can be taken to imply a higher precision than is known, and a result that is written with too few significant figures results in an avoidable loss of precision. Understanding these rules requires a good understanding of the concept of significant and insignificant figures.

Re: How Correlated Are You?

#55

Post by dTal »

crackedbassoon wrote: post #19
tgv wrote: post #4

Surprising article. The conclusion that ear protrusion (yes, that's a thing) has no genetic basis is of course not impossible, but seems unlikely. It would require another data set to establish it.

Ear protrusion is heritable: https://www.nature.com/articles/ncomms8500

Worth noting that not all heritable traits are reflected in DNA, thanks to epigenetics.

Re: How Correlated Are You?

#56

Post by [deleted] »

HDMI_Cable wrote: post #43
Nevermark wrote: post #34

Earlier quoted context omitted.

There is another measurement for men that involves a body part without bone structure. Perhaps this report settles once and for all, what proportion of normally more visible measurements are true correlates with that measurement.

Interesting, it seems like then that the old wives tale—that height, foot size, or hand size—should not correlate with that measurement in men. According to the literature (RIP my search history), height is weakly correlated with it [1], while the ratio between the second digit and fourth digit is moderately correlated [2]. Weirdly enough, the 2D:4D ratio has to do with bone lengths, but is still correlated with that…

[deleted]

Re: How Correlated Are You?

#57

Post by Asraelite »

gnulinux wrote: post #49
triceratops wrote: post #39

Earlier quoted context omitted.

I genuinely have no idea how else you'd pronounce it. "Core-elation"?

I pronounce it a bit like "cor-relation", in IPA like /kor.re'lei.ʃən/ but also perhaps like /ko:.re'lei.ʃən/ ("coh-relation"), and almost never like /ko.re'lei.ʃən/ (~="co-relation"), or never like /ko:r.re'lei.ʃən/ (~"core-relation").

Your IPA transcriptions are a bit off (from what the usual convention for English is anyway). I think you mean /kɑ.ɹəˈleɪ.ʃən/ and /kɔɹ.əˈleɪ.ʃən/ for the first and last, but I'm not sure what you mean by "coh-relation" vs. "co-relation". These seem the same to me, /koʊ.ɹɪˈleɪʃən/.

Re: How Correlated Are You?

#58

Post by crackedbassoon »

dTal wrote: post #55
crackedbassoon wrote: post #19

Earlier quoted context omitted.

Ear protrusion is heritable: https://www.nature.com/articles/ncomms8500

Worth noting that not all heritable traits are reflected in DNA, thanks to epigenetics.

They estimated SNP heritability via GCTA.

Re: How Correlated Are You?

#59

Post by AllenDowney »

krollick wrote: post #48
badcppdev wrote: post #26

"6,000 adult US military personnel (4,082 men and 1,986 women)." .... "Despite the presence of reservists in the sample, it is still not an approximation of the US Civilian population" This dataset is from the US military so please remember that when you think about generalising these results to the rest of the world. Since everyone is talking about it Ear Protrusion 'might' be strongly correlated to height IF you do…

I wonder how many significant dimensions a principal components analysis would identify for that data set, and if one of them would correspond to male/female.

Great question -- it is on my list of ideas for a future article!
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