One generalizable takeaway from this - smaller studies can only show really huge effect sizes. This is what statistical power means - to see small differences b

One generalizable takeaway from this - smaller studies can only show really huge effect sizes. This is what statistical power means - to see small differences between groups you need really large samples. This is fine if you have actually have a huge effect - you probably don't need a large sample size to detect if sugar causes insulin levels to go up in people that are fasting. But if you don't really have large effect sizes in whatever you are studying, then you are screwed. All your statistically significant results are going to have really large effect sizes, because those are the only ones you can see in your small sample size. If you didn't get a significant result, you don't publish, or you stop following up, and if you do get a significant result you publish and do more follow up studies. In time with larger studies or new technology that changes how we can measure things, it becomes apparent that you were chasing noise.