I think it is slightly confusing as to what this statistical phenomenon means in practice. If I understand, there are two interrelated points being made:
1. Because a normal distribution is thin at the tails and thick in the middle, it follows that the % of the curve which is to the right of a right-of-center threshold increases more than the mean itself when the mean shifts to the right.
Ok. So this means the percentage of arbitrarily defined "overweight" people (BMI > 30) increases faster than the average BMI of the average population. This is definitely an interesting observation, but it doesn't make the "increased obesity %" stat wrong in any way. The "BMI > 30" stat never purported to measure the population's average body fat or whatever.
You say the “BMI is a quite decent predictor, or proxy of, body fat %.” And it looks like BMI 30 = around 30% body fat for men. So the issue here should be whether this BMI 30+/30%+ body fat threshold is meaningful for health (or even aesthetic) outcomes? In other words, how does, say, all cause mortality scale with BMI? Maybe 30% body fat is an important inflection point. Or maybe it isn't and the health effects are just linear.
In other words, if the increase in bad health health effects scales at a non-linear rate that is the same or greater than the non-linear scaling of the >30% body fat metric, then it may be entirely appropriate and most accurate to define the "obesity epidemic" by this "arbitrary" metric.
2. If mean BMI has necessarily risen more slowly than the BMI > 30 threshold that has implications for dating the overall increase in BMI and looking for possible environmental causes.
Ok. That seems like an important point. But does that mean that the accelerated increase in the rate of individuals with BMI > 30 is unimportant?
I don't know much about physiology, but I could imagine that there might be some homeostasis effects that might self-regulate hunger and body fat so that population BMI should be "anchored" around some objective healthy level rather than just varying around an (unhealthy) population mean. Even from just 2000 to 2018 you note that the BMI curve is slouching to the right, indicating that the ratio of extreme fatties is accelerating, with a big increase in SD "from 6.6 to 7.8." (Which is seemingly being generated solely by the increase in the right tail of the BMI curve).
In other words, isn't that change in SD prima facie evidence of some real phenomenon that is disproportionately hitting the right tail of the curve? (Or is this a statistical artifact of how SD is calculate for a "normal" curve that isn't fully symmetrical around the mean?)
Anyway, I know you aren't purporting to "solve" the whole issue in this post. But it seems to me that the numbers don't necessarily support Cremieux's claim that the statistics somehow "debunk" the whole "obesity epidemic" narrative.
This might seem off topic but I have a question found this comment on a video it's by Kevin Byrd attacking Uber soy on race and IQ in the comment he tries to defend Lawton from Kevin Bird
This is some embarrassing flailing. I document several misrepresentations and inaccuracies in your video. The claims about the cause of the Flynn effect decline and the relationship of g-loaded IQ subtests and culture were just two direct refutations. You seem very confused about the fact 84% of genes are expressed somewhere in the brain at some point during development. This has no implication for racial differences unless you can specifically identify expression differences between races and their relationship to IQ. This research has not and likely cannot be done and the genetic data I presented shows there is no evidence of substantial genetic differences between races for genes associated with intelligence when you correct for biases in GWAS
I engage with your references the whole time, and bring up studies that address the crucial weaknesses in your cited work. It's a literature review based on some of the latest genetic studies and on economic papers that correct for the shoddy statistical analyses used in much of the IQ literature. It isn't the "sociologist's fallacy to show that accounting for these socioeconomic differences reduces the gaps since there is strong evidence and historical documentation that these socioeconomic differences between races are not genetic themselves and again no evidence from that genetics contributes to these racial gaps. Bringing up the Coleman report is irrelevant when I present papers from this decade (not half a century ago) showing that data from 4 million students pointing toward economic inequality and segregation as driving the majority of achievement test score gap in schools. You should update your references to the proper century.
Now addressing the rest of your tantrum in order
1. Yes, correlational research is weak and needs either experimental validation or more robust methods to infer causality.
2. They are fundamentally interactions, they are not separable as genetic or environmental and they show that phenotypes can
change in different environments. 3. Laughing does not refute my own published researcher showing that genes associated with intelligence do not show the patterns that would be present if natural selection were acting to make Europeans more intelligent than Africans.
4. Your evidence for dysgenics relies on faulty genetic methods prone to false-positives and from researchers with no credibility
or expertise.
5. The sibling study on the Flynn effect is precisely the kind of well-designed study that can distinguish genetic from environmental causes and it unambiguously supports environment and precludes genetic causes.
6. Fst between dog breeds are much larger than between human populations. The paper I cited references 3-5% for human races and 27% for dogs using comparable genetic markers.
7. The distinction between within-and between group heritability is a fundamental aspect of that statistical method. Also the
data I presented did show school districts where there are no racial test score gaps, a closing racial test score gap for national standardized tests, and IQ tests which show no racial gap.
8.I have to once again stress that the "g" in g-factor is not referencing genetics. genetics and the g-factor are largely unrelated
thing. Also, the study about education and gender inequality is not "unknown" and uses data from three well known large studies with representative samples sizes. 9. The Ritchie and Tucker-Drob paper does not show a fade-out effect from these education gains. At least read what you try and
criticize.
10. I cited papers that controlled for income and wealth and they accounted for nearly the entire gap in academic performance. 11. That paper 1 cited is literally the main reference in your own review paper, along with a large single-cohort study of 18,000 people showing a correlation of 0.27, which the authors settle on as the most likely value.
The problem here is you don't understand genetics or evolution or how these methods work, while I do. This is also the issue between intelligence "experts" and actual geneticists and evolutionary biologists. The GWAS work cited is extremely flawed, this has been argued to death in the literature and I directly tested it in my paper and showed it produced false-positive results.
Finally, none of the work I cited failed to replicate and Richard Lewontin was one of the most influential evolutionary biologists of the late 20th century and Lewontin's fallacy is a complete misnomer
For health status, it’s important to know the amount of visceral adipose tissue (VAT). For that, waist circumference is important. Body roundness index (BRI) has been proposed in the literature, which is similar to a waist-height ratio. I’m working on a paper to estimate VAT from these measurements plus accelerometry (2011-2014 nhanes).
I think there are possible low-cost alternatives to dexa, like ultrasound. But, the fact is that you can estimate VAT pretty accurately with just height, weight, and waistline. R>0.8.
How about kurtosis? It looks to me as if men have more extremely fat people, but excluding those, male variance is actually less. The men would have a smaller interquartile range?
With technology like the Styku and DEXA, measuring body fat percentage is pretty easy. I prefer it, as I’m short and I lift weights, so my BMI says I’m overweight, but my percentage of body fat puts me in the top 5% of my age group.
> a change in the rate of brain in obesity rate is expected
lol
I think it is slightly confusing as to what this statistical phenomenon means in practice. If I understand, there are two interrelated points being made:
1. Because a normal distribution is thin at the tails and thick in the middle, it follows that the % of the curve which is to the right of a right-of-center threshold increases more than the mean itself when the mean shifts to the right.
Ok. So this means the percentage of arbitrarily defined "overweight" people (BMI > 30) increases faster than the average BMI of the average population. This is definitely an interesting observation, but it doesn't make the "increased obesity %" stat wrong in any way. The "BMI > 30" stat never purported to measure the population's average body fat or whatever.
You say the “BMI is a quite decent predictor, or proxy of, body fat %.” And it looks like BMI 30 = around 30% body fat for men. So the issue here should be whether this BMI 30+/30%+ body fat threshold is meaningful for health (or even aesthetic) outcomes? In other words, how does, say, all cause mortality scale with BMI? Maybe 30% body fat is an important inflection point. Or maybe it isn't and the health effects are just linear.
In other words, if the increase in bad health health effects scales at a non-linear rate that is the same or greater than the non-linear scaling of the >30% body fat metric, then it may be entirely appropriate and most accurate to define the "obesity epidemic" by this "arbitrary" metric.
2. If mean BMI has necessarily risen more slowly than the BMI > 30 threshold that has implications for dating the overall increase in BMI and looking for possible environmental causes.
Ok. That seems like an important point. But does that mean that the accelerated increase in the rate of individuals with BMI > 30 is unimportant?
I don't know much about physiology, but I could imagine that there might be some homeostasis effects that might self-regulate hunger and body fat so that population BMI should be "anchored" around some objective healthy level rather than just varying around an (unhealthy) population mean. Even from just 2000 to 2018 you note that the BMI curve is slouching to the right, indicating that the ratio of extreme fatties is accelerating, with a big increase in SD "from 6.6 to 7.8." (Which is seemingly being generated solely by the increase in the right tail of the BMI curve).
In other words, isn't that change in SD prima facie evidence of some real phenomenon that is disproportionately hitting the right tail of the curve? (Or is this a statistical artifact of how SD is calculate for a "normal" curve that isn't fully symmetrical around the mean?)
Anyway, I know you aren't purporting to "solve" the whole issue in this post. But it seems to me that the numbers don't necessarily support Cremieux's claim that the statistics somehow "debunk" the whole "obesity epidemic" narrative.
> This is part of the usual greater male variance phenomenon.
I once tried to research this, but it seemed that it is controversial as a subject. What should I read to get a good sense of it?
> This is part of the usual greater male variance phenomenon.
SD height/average height ratio is smaller in males. Why absolute SD is better than relative?
SD already compares wirh average. Remember, SD can be seen as the average deviation from the average.
Why would you ratio SD with mean?
there are values where we make plots with log(value) instead of value.
If the result changes whether we take log or not, this cannot be "higher male variability".
Isn't WHR waist-to-hip ratio?
This might seem off topic but I have a question found this comment on a video it's by Kevin Byrd attacking Uber soy on race and IQ in the comment he tries to defend Lawton from Kevin Bird
This is some embarrassing flailing. I document several misrepresentations and inaccuracies in your video. The claims about the cause of the Flynn effect decline and the relationship of g-loaded IQ subtests and culture were just two direct refutations. You seem very confused about the fact 84% of genes are expressed somewhere in the brain at some point during development. This has no implication for racial differences unless you can specifically identify expression differences between races and their relationship to IQ. This research has not and likely cannot be done and the genetic data I presented shows there is no evidence of substantial genetic differences between races for genes associated with intelligence when you correct for biases in GWAS
I engage with your references the whole time, and bring up studies that address the crucial weaknesses in your cited work. It's a literature review based on some of the latest genetic studies and on economic papers that correct for the shoddy statistical analyses used in much of the IQ literature. It isn't the "sociologist's fallacy to show that accounting for these socioeconomic differences reduces the gaps since there is strong evidence and historical documentation that these socioeconomic differences between races are not genetic themselves and again no evidence from that genetics contributes to these racial gaps. Bringing up the Coleman report is irrelevant when I present papers from this decade (not half a century ago) showing that data from 4 million students pointing toward economic inequality and segregation as driving the majority of achievement test score gap in schools. You should update your references to the proper century.
Now addressing the rest of your tantrum in order
1. Yes, correlational research is weak and needs either experimental validation or more robust methods to infer causality.
2. They are fundamentally interactions, they are not separable as genetic or environmental and they show that phenotypes can
change in different environments. 3. Laughing does not refute my own published researcher showing that genes associated with intelligence do not show the patterns that would be present if natural selection were acting to make Europeans more intelligent than Africans.
4. Your evidence for dysgenics relies on faulty genetic methods prone to false-positives and from researchers with no credibility
or expertise.
5. The sibling study on the Flynn effect is precisely the kind of well-designed study that can distinguish genetic from environmental causes and it unambiguously supports environment and precludes genetic causes.
6. Fst between dog breeds are much larger than between human populations. The paper I cited references 3-5% for human races and 27% for dogs using comparable genetic markers.
7. The distinction between within-and between group heritability is a fundamental aspect of that statistical method. Also the
data I presented did show school districts where there are no racial test score gaps, a closing racial test score gap for national standardized tests, and IQ tests which show no racial gap.
8.I have to once again stress that the "g" in g-factor is not referencing genetics. genetics and the g-factor are largely unrelated
thing. Also, the study about education and gender inequality is not "unknown" and uses data from three well known large studies with representative samples sizes. 9. The Ritchie and Tucker-Drob paper does not show a fade-out effect from these education gains. At least read what you try and
criticize.
10. I cited papers that controlled for income and wealth and they accounted for nearly the entire gap in academic performance. 11. That paper 1 cited is literally the main reference in your own review paper, along with a large single-cohort study of 18,000 people showing a correlation of 0.27, which the authors settle on as the most likely value.
The problem here is you don't understand genetics or evolution or how these methods work, while I do. This is also the issue between intelligence "experts" and actual geneticists and evolutionary biologists. The GWAS work cited is extremely flawed, this has been argued to death in the literature and I directly tested it in my paper and showed it produced false-positive results.
Finally, none of the work I cited failed to replicate and Richard Lewontin was one of the most influential evolutionary biologists of the late 20th century and Lewontin's fallacy is a complete misnomer
(https://onlinelibrary wiley.com/doi/abs/10.1002/bies.202100204)
For health status, it’s important to know the amount of visceral adipose tissue (VAT). For that, waist circumference is important. Body roundness index (BRI) has been proposed in the literature, which is similar to a waist-height ratio. I’m working on a paper to estimate VAT from these measurements plus accelerometry (2011-2014 nhanes).
isn't WHR waist-to-hip ratio?
Yes, but the NHanes data set does not have this measurement.
I think there are possible low-cost alternatives to dexa, like ultrasound. But, the fact is that you can estimate VAT pretty accurately with just height, weight, and waistline. R>0.8.
Why bother with measuring tape when you can just use a DEXA or Styku?
Because I have a measuring tape in my drawer and it doesn’t cost hundreds of dollars and require thousands of dollars worth of equipment.
That’s a good answer! My gym has a Styku, and I get a monthly scan with the personal training package.
How about kurtosis? It looks to me as if men have more extremely fat people, but excluding those, male variance is actually less. The men would have a smaller interquartile range?
With technology like the Styku and DEXA, measuring body fat percentage is pretty easy. I prefer it, as I’m short and I lift weights, so my BMI says I’m overweight, but my percentage of body fat puts me in the top 5% of my age group.