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Aella
@Aella_Girl
Women in science need to learn more about how statistics work. If I published results this low in significance as tho they were real I could have 'proved' eighty different insane things by now. To explain why this is bad:
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Emily Khazan
@EmilyKhazan
It can be hard being a #womaninscience, and teaching evaluations often don't help. We show gender bias in teaching reviews of graduate students; another subtle force making the leaky pipeline leakier: insidehighered.com/news/2020/11/0
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Aella
@Aella_Girl
Her sample size is low. There's calculations you can do to figure out exactly how meaningful a result (basically, what's the probability it was an "accidental" positive) is based on the sample size and the correlation strength. It's irresponsible to publish results as significant
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Aella
@Aella_Girl
if there's too high a probability that your correlation was just random chance. If you test a lot of things for long enough, you're absolutely gonna find lots of correlations that are just random chance! This means you have to be extra careful if you're checking a ton of these.
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anton 🏴‍☠️
@atroyn
Replying to
@Aella_Girl
this is really what should be taught in science ethics classes;
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anton 🏴‍☠️
@atroyn
a large part of the problem is people mentally substitute “science” with “randomized double blind study on 12 people a psych grad student slapped a p value on and interpreted to mean whatever gets them published”
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anton 🏴‍☠️
@atroyn
Replying to
@atroyn
and
@Aella_Girl
but in the current culture of the broader academy, the incentive is to do what gets you published; it’s often not really the researcher’s fault.
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anton 🏴‍☠️
@atroyn
Replying to
@Aella_Girl
there’s a lot at play here; in the social sciences it seems to be the case that researchers focus on quantitative metrics to gain legitimacy by resembling physical sciences for example
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