The thing is, how would you distinguish a world in which the female population of said high-school are missing five centimeters and 4 points of IQ due to dieting from the one we inhabit? Where do we get a baseline from? Arrgh.
The thing is, how would you distinguish a world in which the female population of said high-school are missing five centimeters and 4 points of IQ due to dieting from the one we inhabit? Where do we get a baseline from?
That’s no great mystery.
Contemporary medicine has a pretty good idea about what kind of deficiencies, caloric and otherwise, stunt growth and IQ. There is a trove of empirical data available, most of it from the third world, and the subject is well-researched.
Moreover, the second half of the XX century provided a few natural experiments in which some chronically malnourished populations stopped being malnourished (Japan and Korea come to mind) so we know quite well how it works and what to expect.
If you want to do your own empirical research, that’s easy, too. All you need is a data set of height and weight for a sample of teenage girls. Off the top of my head, you would start by temporarily discarding the left tail of the distribution (the could-possibly-be-underweight girls) and estimating the scaling factor, the weight/height ratio. That’s basically how BMI works except that they got the factor somewhat wrong (for ease of pen-and-paper calculation). Once you know the known-to-be-not-malnourished scaling factor, you go back to the full data set and use the factor to calculate the “fatness” (aka height-adjusted weight) from height and weight—again, that’s basically BMI—and plot height on the Y axis and fatness of the X axis.
If your sample is big enough (or drawn from the appropriate population), you would see that at the extremes the relationship between fatness and height would break down—in the left tail that would be when malnourishment seen here as very low fat would affect height. Take a sample from someplace like Somalia and you should be able to see it in empirical data easily enough. Take a sample from a Western country and you would have to go far into outliers in the left tail to see that breakdown if it’s there at all.
People with stunted growth likely exist in the West, but their numbers are miniscule.
There are certain statements where we don’t know whether or not they are true. They might be true. They also might not be true.
If I think of an hypothesis that compatible with my understanding but where I don’t have certainty that it’s true “might” is the appropriate word.
If you think that there no way that it’s true than it should be possible for you to prove that it’s not true. For me proving that you can’t prove that it’s not true for claim where I don’t know whether they are true is a complicated matter.
The thing is, how would you distinguish a world in which the female population of said high-school are missing five centimeters and 4 points of IQ due to dieting from the one we inhabit? Where do we get a baseline from? Arrgh.
That’s no great mystery.
Contemporary medicine has a pretty good idea about what kind of deficiencies, caloric and otherwise, stunt growth and IQ. There is a trove of empirical data available, most of it from the third world, and the subject is well-researched.
Moreover, the second half of the XX century provided a few natural experiments in which some chronically malnourished populations stopped being malnourished (Japan and Korea come to mind) so we know quite well how it works and what to expect.
If you want to do your own empirical research, that’s easy, too. All you need is a data set of height and weight for a sample of teenage girls. Off the top of my head, you would start by temporarily discarding the left tail of the distribution (the could-possibly-be-underweight girls) and estimating the scaling factor, the weight/height ratio. That’s basically how BMI works except that they got the factor somewhat wrong (for ease of pen-and-paper calculation). Once you know the known-to-be-not-malnourished scaling factor, you go back to the full data set and use the factor to calculate the “fatness” (aka height-adjusted weight) from height and weight—again, that’s basically BMI—and plot height on the Y axis and fatness of the X axis.
If your sample is big enough (or drawn from the appropriate population), you would see that at the extremes the relationship between fatness and height would break down—in the left tail that would be when malnourishment seen here as very low fat would affect height. Take a sample from someplace like Somalia and you should be able to see it in empirical data easily enough. Take a sample from a Western country and you would have to go far into outliers in the left tail to see that breakdown if it’s there at all.
People with stunted growth likely exist in the West, but their numbers are miniscule.
Such a world would not have an incredible 30.4% of girls aged 2-19 being overweight or obese.
Like Lumifer, I am stunned at the notion of today’s high-school girls being dangerously underfed.
A overweight girl who eats too much for a few months then diets and eats to little for a few months might still get negative effects on height and IQ.
You got evidence for that statement?
Statements qualified with “might” inherently don’t need evidence.
Yes, they do.
Prefacing nonsense with “might” does not make it any less nonsense.
There are certain statements where we don’t know whether or not they are true. They might be true. They also might not be true.
If I think of an hypothesis that compatible with my understanding but where I don’t have certainty that it’s true “might” is the appropriate word.
If you think that there no way that it’s true than it should be possible for you to prove that it’s not true. For me proving that you can’t prove that it’s not true for claim where I don’t know whether they are true is a complicated matter.
Well, ones like “Mortimer Q. Snodgrass might be the culprit” do.