If all laymen knew that n is irrelevant, it's the effect size that matters, we would be in a world of good.
If the difference between means is large compared to the standard deviation of the population under study, you need less n.
Another fun fact: All groups are different given large enough n.
Edit: I'm not endorsing the study - just pointing out that n should not be the most critical thing you look for when evaluating a study. The study is fine as a simple quasi-experiment, testing the hypothesis by induction. I don't see anyone claiming causal links here. Now a study testing the hypothesis deductively needs to be designed.
If the difference between means is large compared to the standard deviation of the population under study, you need less n.
Another fun fact: All groups are different given large enough n.
Edit: I'm not endorsing the study - just pointing out that n should not be the most critical thing you look for when evaluating a study. The study is fine as a simple quasi-experiment, testing the hypothesis by induction. I don't see anyone claiming causal links here. Now a study testing the hypothesis deductively needs to be designed.