Small n studies are often the most exciting. If you get a p<0.01 effect in 10 people vs. 10000 people... the lower bound of the effect size is similar, and so for the same prior each study should change our belief by a similar amount... but the upper bound of effect size is much larger in the large study.
The only thing that messes this logic of mine up a bit is publication bias. There's a much larger chance that small studies sit on the shelf unpublished if there's no clear effect.
Small n studies are often the most exciting. If you get a p<0.01 effect in 10 people vs. 10000 people... the lower bound of the effect size is similar, and so for the same prior each study should change our belief by a similar amount... but the upper bound of effect size is much larger in the large study.
The only thing that messes this logic of mine up a bit is publication bias. There's a much larger chance that small studies sit on the shelf unpublished if there's no clear effect.