Because you can get there so much faster if you use a big machine to throw you right over annoying factual hurdles in your way.
I’ve been meaning to dissect this issue for over a week, but a lot of things have been happening over here. A recent news story has prompted a lot of discussion, some of it rather ugly. The short of it (and the news article in the Chicago Tribune is not terribly long) is that a 29-year old woman identified only as “K.E.J.” has been granted an appellate opinion in her favor. The woman experienced a traumatic brain injury as a child, and according to the wording of the article, “cannot be left alone to operate a stove or perform most household chores”, although by having that bit of information alone, our perceptions of her are biased because it does not mention what she is capable of doing. Her legal guardian, an aunt, had filed a petition with the court to have her (fallopian) tubes tied. All three judges on the panel were unanimous in their decision against this action.
“Tubal ligation is a particularly drastic means of preventing a mentally incompetent ward from becoming pregnant,” Judge Joseph Gordon wrote in the 36-page opinion. There are “less intrusive and less psychologically harmful [birth-control] alternatives.”
The readers’ comments were much longer than the article, and many were downright rude. This situation is so fraught with over-generalisations and false dichotomies and conflations that it fair makes me dizzy. The biggest and most common fallacy of the lot was the combined Continue reading Catapulting to Conclusions
Whenever I read statistics about the “increasing rates of autism”, I heave a big sigh. Those statements invariable contain a whole number of assumptions, many of them flat-out wrong, or at least unexamined. In the epidemiological data, there are diagnostic issues and census issues and statistical issues and of course, the inevitable agenda issues in the reportage of the census results and analyses. I’ve previously discussed a number of these problems, including incidence versus prevalence, and correlation versus causality in the post, “Epidemics of Bad Science vs Epidemics and Bad Science”
What I would like to address today is a related issue with diagnostics and perceived prevalence, meaning, “How do we know who has autism or AD/HD or a learning disability, and how many such people are out there?”
In entomology (and in other zoological branches) we have a concept known as “trap bias”. There are a number of ways of taking a census of an animal population, including using traps. A “trap bias” means that the kind of trap you use to census a population will limit the responders to your census, and thus create unintended biases in the results.
Now, if a few synapses in your brain just fizzled from that wordy definition, let’s try a simple example. Continue reading More "Trap Bias"
Wow. Here I was ready to comment on one piece of news, when several more caught my attention. They all revolve around social ideas of gender rôles, and marginalised or disabled people.
This first one struck close to home: Khadijah Farmer was kicked out of women’s toilet of a Manhattan, NY, restaurant because the bouncer thought she looked too masculine.
“I said, ‘I am a woman and I am where I am supposed to be,'” said Farmer, speaking at a a news conference. “I offered to show him some identification. I was told that’s neither here nor there.”
Some people might say that happened “just because” she’s a lesbian (like that’s a valid reason), but I can vouch for the same thing happening to me as well. On the occasion that I wear a skirt or dress, I look “appropriately” female. But since I have a really short hair style, and often wear men’s shoes (because I have wide feet) and men’s shirts (because I have broad shoulders and long arms) and am disinclined toward wearing make-up, I have been frequently mistaken for a guy.
Even my name doesn’t seem to help; just last week Continue reading M, F, N/A