Science does not rely on authority as an indicator of truth.

Ben Goldacre: Battling bad science

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It is worth reiterating that contrary to popular depictions of science, science does not rely on authority as an indicator of truth.

The video reminds me of an xkcd comic showing the problem with using statistical significance if the studies showing no effect are unreported.

In this analogy, the study showing a link between green jelly beans and acne has only a 5% probability (or less) of being a coincidence (p < 0.05). This would be convincing evidence that there is a link between green jelly beans and acne, except all the 19 studies showing no link between non-green jelly beans and acne were unreported and discarded. If all the study results were reported, then it would suggest that the result of the green-jelly-bean study is indeed a coincidence: 1/20 = 5%.

Scientific studies in real life can be even worse. Companies, and even university researchers, are not obligated to publish studies in which the results show no effect (studies with “null results”). This means that researchers can run the hypothetical green-jelly-bean study 20 times until they get the result that they want, by coincidence. What normally happens does not involve ill intent, but has the same effect. The hypothetical green-jelly-bean study is run independently by 20 different research teams (who can be separated by time), who are unaware of each other, because studies with negative results are not published. Only the group with the positive result publishes its results, but the result is actually a coincidence. See the concept of publication bias at Wikipedia.
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We cannot name ourselves without Othering ourselves in the White Gaze.

In Bad Romance: Feminism and women of colour make an unhappy pair, Sana Saeed writes:

“Women of colour” beautifully illustrates the exact problem I discovered with feminism, as a woman who did not fit the mainstream criteria for being just a Woman. As a “woman of colour,” I am not just a Woman. I am a woman with a little something extra; there is a difference struck between women like me and white women. There is no Woman. There are no Women. There are two groups: women and “women of colour.” This tidily, and unfortunately, translates into the “us” and “them” categorization.

Because this distinction is made and has been proudly appropriated by “women of colour” without much criticism, this presumption that the white woman’s identity is a sort of “foundational” identity for all women is prevalent within feminism.

According to Loretta Ross, however, the term “women of color” was coined in 1977 among some black and other “minority” women in Washington, DC as “a solidarity definition, a commitment to work in collaboration with other oppressed women of color who have been ‘minoritized’.” Ross says, “Unfortunately, so many times, people of color hear the term ‘people of color’ from other white people that [PoCs} think white people created it instead of understanding that we self-named ourselves.”

However, regardless of its history, Sana makes a salient point: the term “woman of colour” suggests “a woman with a little something extra”, which implies that whiteness is the default.

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Across the calculus sections, women outperformed men on grades.

Several recent studies have suggested that the gender gap in STEM fields is caused not by bias, but simply by different choices made by men and women. What the new research shows, Dasgupta said, is choice isn’t as simple as people think. “People assume that these choices are free choices, based on talent and interest and motivation,” Dasgupta said. “But these data suggest that the meaning of choices, of what it means to choose math or science, is more complicated. Even talented people may not choose math or science not because they don’t like it or are not good at it, but because they feel that they don’t belong.”

Inoculation Against Stereotype by Scott Jaschik (Inside Higher Ed)

There is a common belief among some computer geek communities that women are underrepresented in STEM because we just don’t like it, and so we should celebrate differences instead of making women “miserable” by “forcing” us into careers we “don’t like”. This study would debunk that myth, if only most men in tech who discuss the topic of women in tech actually did some research on it, instead of leaving comments that make male geeks feel good about themselves and rationalize the gender imbalance in “their” field.

For other male geeks who insist that there are hard-wired brain differences in men and women, and argue that women’s brains are hard-wired against understanding math and science as well as men (instead of hard-wired against enjoying math and science), this part of the article should be emphasized:

Skeptics might wonder if some of the [gender] differences [in engagement] among students relate to how well the students know the material. The researchers checked for that and found that, across sections, women outperformed men on grades. So the data point to women losing confidence with male instructors — even if female students know the material as well as or better than their male counterparts.

Link: Inoculation Against Stereotype (Inside Higher Ed)

Men agree to casual sex more, because female strangers are not considered dangerous and bad in bed.

Or (Heterosexual) Male privilege, not evolution or innate female frigidness, explains the gender difference in accepting random propositions for casual sex.

Gender Differences and Casual Sex: The New Research:

[M]ost of the gender difference in women’s and men’s propensity to agree to a broad-daylight, out-of-nowhere proposition for casual sex is driven by women’s perception that their risks are higher, and their likely enjoyment is lower from the proposer.

In the actual paper, Conley (2011) concludes:

First, male sexual proposers (who approached women) are uniformly seen as less desirable than female sexual proposers (who approached men). Therefore, gender differences in the original Clark and Hatfield study are due more to the gender of the proposer than to the gender of the study participants. Moreover, the idea that these gender differences reflect broad, evolved differences in women’s and men’s mating strategies was not supported. Across studies involving both actual and hypothetical sexual encounters, the only consistently significant predictor of acceptance of the sexual proposal, both for women and for men, was the perception that the proposer is sexually capable (i.e., would be “good in bed”). The perceptions of sexual capabilities also mediated the relationship between gender and acceptance of casual sex offers. Finally, indirect evidence suggests that perceptions of risk may play a role in gender differences in casual sex attitudes.

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White Americans did NOT elect Obama.

This myth won’t die:

But not everyone buys that script. Mona Charen, a conservative columnist for the National Review, challenges that view with this question: If more white Americans feel like an embattled minority, why did they elect President Barack Obama?

“Did they become racist after electing the first black president?” she asks.

Charen says the United States today is “incredibly tolerant and open.”

White Americans did not elect Obama. Most White Americans (55%) voted for McCain. Obama was elected by most Americans of color and a minority (43%) of White Americans.

Yes, the numbers can and do work like that.

Othering and Projection: Chinese is confusing vs. Chinese are confused

In English, a person says, “It’s all Greek to me,” when they do not understand the words of someone else. In Greek, when a person does not understand, they say it sounds like Chinese. Many languages have an expression that names another language as epitome of unintelligibility. It turns out that in a directed graph, most languages converge on Chinese as the unintelligible language.

Directed graph shows various languages as nodes with arrows pointing at other languages, eventually pointing to the 'Chinese' node. The 'Chinese' node points to 'Heavenly Script'.

This is understandable. Chinese writing, especially Traditional Chinese, is very visually complex. Chinese characters are logograms, which makes learning how to read Chinese difficult.

However, there is a difference between finding Chinese writing confusing and alleging that Chinese people are confused.

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This is an example of sexism in tech recruitment.

This is an example of unconscious sexism in tech recruitment that assumes that women are bad with math and computers.

A public transit ad shows a brain with two hemispheres. A box pointing to the left hemisphere asks, 'Can you solve one of our puzzles?' A box pointing to the right hemisphere asks, 'Can you explain it to your mom?' Text at the bottom says, 'We're hiring hackers with people skills.' There is a real yellow sticky note stuck on to the ad that says, 'My mom has a PhD in math.'

The yellow sticky note says, “My mom has a PhD in math”.

Close up of yellow sticky note that says, 'My mom has a PhD in math'

I am not a mother, but if I reproduced, I would be.

The job ad is also based on the same stereotype of female technical ineptitude as “So simple, your mother could do it”.

Original photo by Jessie Bennett (via Sociological Images and Geek Feminism Blog)