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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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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But also it's extra terrible that this author reported a meaningful result when she admits it's not statistically significant! I'm physically recoiling from this idea. How are they training people in school that this is something they think is ok?
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Science takes patience
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The best example of misunderstood statistics and how they need to be put in context is the fact that just about everyone has an above average number of arms.
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I think it would have been much more helpful if you had posted the original article this is referring to, rather than a popular science one written about it. https://sci-hub.do/10.1080/02602938.2020.1724875 …
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And furthermore, a published paper with mistakes is not a "women in science issue" - it is a "why did this paper publish this with obvious errors?"
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I am not sure what you are getting at at all - the paper addresses a weak correlation between SETs and true instructional quality and uses model simulations to demonstrate that SETs, even the best possible SETs are kind of useless because of systemic biases from students.
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Do you see the irony of how this ONE woman who uses a low sample size and flexible analysis somehow means that “women in science need to learn more about how statistics work”? There are plenty of extraordinary women in science, just look at this year’s Nobel winners.
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Yeah, but beyond that - this is not an example of ONE woman using low sample sizes - I honestly think
@Aella misunderstood this study.
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