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Engineers Have More Sons, Nurses More Daughters
Bifurcati writes "While it might be irrelevant for many /.ers, a recent study has shown that people in stereotypically male professions (engineering, IT, mathematics, etc) are more likely to have sons than daughters, while nurses, therapists and teachers tend to produce more girls. Based on independent survey data, engineering types produce 140 boys to every 100 girls, while nurses and the like produce 135 girls to 100 boys. The explanation is unclear, but it might have interesting long-term social implications. A more detailed summary of the journal article is available on Illuminating Science."
There are proponents of different techniques that supposedly let you choose the sex of your child. One interesting technique is called the Shettles Method. One family that I know swears by this method. They are four for four in getting it to work.
At any rate, perhaps different personalities or lifestyle conditions between engineers and nurses would help to explain this data - if indeed there is any credence to Shettles or similar methods.
Ha, ha! Nobody ever says Italy.
Well you generally have an equal number of X and Y sperm (while all eggs are X of course) its been shown that Y sperm die easier when conditions are harsh (acidity, not right temperature etc.) and are stronger when conditions are just right. So this affects the gender greatly. How brain-type affects gender is unknown but probably based on hormons levels which can change these conditions.
There is no doubt that a babies sex can be influenced by a number of criteria. Male sperm tends to be faster, but live shorter lives. Female sperm is hardier, but slow. So a women who is slightly acidic or base will tend to kill the male sperm leaving female sperm. Likewise, if traditional sex prevails (male on top) with a laying around afterwards, then male has better chance (shorter distance, as gravity helps carry the sperm further up (BTW, so does a women's orgasm). But if women on top, then sperm has further to go, so more likely that female sperm wins.
So why relevant? Nurses, teachers, etc have a healthier attitude about sex. More likely the women are on top (or at least have a varied sex life). Girl wins.
Engineers are more conservative, so more likely to be on top. Boy wins.
I prefer the "u" in honour as it seems to be missing these days.
Or more importantly, who is doing it.
We have two groups of children: One group has a parent who is in a "male" profession, like engineering, and the other has a parent in a "female" profession, like nursing.
What is far more likely to be true of a child with a parent who is in a female profession as opposed to a child with a parent in a male profession?
They're more likely to have a mother who works.
Seems pretty obvious to me: Working moms are more likely to have girls. Might have something to do with Y-chromosome sperm being more fragile than X-chromosome sperm. (That's been demonstrated elsewehre.)
paintball
The question, of course, is whether this is a reasonable interpretaiton of an objective set of data, or whether this is pseudostatistics where you start from a conclusion, and work backwards to find it in the numbers. Some questions I'd like to see addressed:
* How were the groupings into "masculine" and "feminine" professions done? Is this reasonable, and did they truly choose the most "obvious" masculine and feminine professions to include?
* Do these groupings span the dataset, or are some (possibly most) professions excluded as "neutral"?
* What is the breakdown by profession for all professions, not just the included groups?
* Most importantly, was the selection of the "masculine" and "feminine" professions determined BEFORE or AFTER the data was collected?
My concern here is that they started with a dataset for chilbirth for all professions (probably on a fairly small dataset). They noticed some professions skewed one way, some another. They noticed that some of the professions skewing male were "masculine" and some skewing female were "feminine" and called it a conclusion, sweeping all the other anomalites in their dataset under the rug. Hey, presto! Conclusion!
Fact: The general benchmark for "statistical significance" is 95% confidence that the data cannot be explained as a random phenomenon.
Experiment: Create 20 hypothetical correlations to test for on a completely random dataset. On average, you should find one in twenty hits the 95% confidence mark.
Intellectually dishonest followup: Publish your one statistically significant result with great fanfare. Bury the othe 19 in a footnote, if you mention them at all.
Step 3: Profit!