moominmolly | (no subject)

From the NurtureShock article Why Teenagers Are Growing Up So Slowly Today:

Here’s a Twilight Zone-type premise for you. What if surgeons never got to work on humans, they were instead just endlessly in training, cutting up cadavers? What if the same went for all adults – we only got to practice at simulated versions of our jobs? Lawyers only got to argue mock cases, for years and years. Plumbers only got to fix fake leaks in classrooms. Teachers only got to teach to videocameras, endlessly rehearsing for some far off future. Book writers like me never saw our work put out to the public – our novels sat in drawers. Scientists never got to do original experiments; they only got to recreate scientific experiments of yesteryear. And so on.

Rather quickly, all meaning would vanish from our work. Even if we enjoyed the activity of our job, intrinsically, it would rapidly lose depth and relevance. It’d lose purpose. We’d become bored, lethargic, and disengaged.

In other words, we’d turn into teenagers.

Yeah, that sounds about right.

Flat | Top-Level Comments Only

I can't use a different term, those are the data that I'm reporting.

Why do you consider that term (or is it the data, I can't tell) intellectually dishonest? I can see how another datum would more useful in some contexts, but not how this datum (or term?) is dishonest.

rezendi, if you made it past one year life expectancy goes up. Past five years, up again. Past 60, way up again.

It's merging two different statistical blocks that are not related.

The younger one has a decreasing "percent chance of dying each year" because infants are more likely to die of any number of conditions (including bad genetic lottery: being not capable of maintaining life outside the womb) - pre-adolescents have the lowest mortality rate.

The older group has an increasing chance of dying each year due to such causes as teenage low-impulse control, giving birth, progressively weakening immune system, cancer, war, etc.

In other words: They die for different reasons, and taking a total average instead of separating out the populations is like saying "The average American has one ovary and one testicle" - which is statistically very close to true, but doesn't describe the experience or reality of any real person. Or like saying, "The average person has fewer than 2 but more than 1 hands" - also true, but meaningless - where a better analysis would be "X% of Americans have fewer than 2 hands, and of those, Y% have no hands at all."

Similarly, the standard "life-expectancy at birth" is useful only for whole-population averages, as in "Estimate number of children born live per woman in France in 1752". And that still doesn't take into consideration such factors as nuns or old maids who never give birth, or the birth-count disparity between peasants and nobility.

If you want to trot out examples of how long people lived, you have to focus on a single distinct population - only count population after the inflection point in the "percent chance of dying this year" curve, or better yet, separate out men from women, and separate out women who died in childbirth and men who died in warfare, separate out instances of widespread plague or famine, etc. The more you generalize, the less you know.

The more you generalize, the less you know.

This makes sense to me, but the rest of what you say seems to be more along the lines of "life expectance at birth is a very general measure that hides a lot of variation and therefore isn't very exact." Which is different than "life expectancy at birth has no statistical validity" which is how I interpreted your original statement.

Similarly, the standard "life-expectancy at birth" is useful only for whole-population averages,
I was talking about whole populations so, er, where's the problem?

LEaB is bad statistics because it takes an average of two separated datasets - it's a bimodal distribution (http://en.wikipedia.org/wiki/Bimodal), and the "average" is not representative of a typical person.

If, in pre-modern times, the true life expectancy was 30 (that is, most people could be expected to live until 30 but not much longer), then that puts humans (with steady food supply, language and the ability to negotiate, medical practices) at worse than wild chimpanzees (http://en.wikipedia.org/wiki/Chimpanzee#Anatomy_and_physiology)! How did this discrepancy arrive? Because we don't count the mortality of young chimps in the same group as the expected lifespan of grown chimps, which gives a better indicator of how long a grown chimp is likely to live.

Mostly, see Life Expectancy (http://en.wikipedia.org/wiki/Life_expectancy) and note the distinction that even in the Upper Paleolithic, life expectancy for an adult was 54 years old. But you only get that number if you exclude infant mortality (http://en.wikipedia.org/wiki/Infant_mortality).

(no subject)

no subject

Bad statistics

Re: Bad statistics

Re: Bad statistics