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Notes -
The study…is that really the fully study, or just the abstract…has a total of 48 infants. And the primary criteria is quite subjective—besides blink rate, it was all unquantified “oh the baby struggled more quickly”. (And even the blink rate isn’t actually display in a table anywhere.)
And, it’s the least blinded study I could imagine. The authors quite obviously knew they were looking at white or Asian babies, so there’s a huge potential for bias…up to and including pushing some of the babies harder.
That poster has made top level posts mentioning these unresponsive Chinese babies twice now. Those claims were based on this study? That's how far they strained themselves in searching for sources?
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I looked around for copies in other aggregators and it seems to me that is actually the entire study. It's less than 1 page! Being a researcher in the 60's must've been fun.
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And with this small sample, they nevertheless got massively significant p-value of 0.0001. Small sample size makes it harder for p-values to reach significance.
That's why the discuss the reliability:
So, they are quite subjective, but the authors subjective judgements were in very high agreement.
I have no idea whether this study if correct or not, but why are you cherry picking the very lowest p-value of the several reported?
It's sufficient to dispose of the argument that the study should be discounted because of its low sample size (which is an innumerate argument that gets thrown around far too often on the internet). P-values are, in part, a function of sample size. They're the answer to the question "what is the likelihood of seeing a pattern at least this strong in a sample of this size under the null hypothesis?". Having a small sample size isn't some sneaky hack to get more statistically significant results - as wlxd points out, a smaller sample size makes it harder to find significant results (i.e. you need a stronger effect size).
A lot of people have this vague idea that a study needs thousands or tens of thousands of observations to get persuasive results about some statistical pattern, and it's just not true. As an intuition pump, imagine flipping a coin 48 times and getting 42 heads and 6 tails. Is that not enough to convince you that the coin (or flipping process) is rigged?
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Oh please that was the lowest…but barely.
I suppose it’s a bit cherry-picking to point out the lowest p-value, but all of the subjective observations were in that teeny tiny range.
Well, no, not barely. It was the lowest by a lot. The other p-values were small, but nowhere near that small (they were from .005 to .06). Besides, if they were all about the same, then why not cite the highest? That's what people do when they argue in good faith.
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