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.






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Across the calculus sections, women outperformed men on grades.


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





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This is an example of sexism in tech recruitment.






