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Notes -
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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