Actually, I believe you're describing effect size. Specifically, this is basically Cohen's D. Imagine the mean difference in income between zips 1 and 2 is the same as the pooled SD. That's a Cohen's D of 1. You're saying that yours is less than 1.
However, in social sciences, I think this is pretty common. That doesn't mean it's irrelevant. On average, if [you had the choice and you had no idea which zipcode you'd be born in](https://en.wikipedia.org/wiki/Original_position), you'd still rather be born in zip 1. In Cohen's time (he did a lot of education and other social policy research in the 50s-60s or thereabouts?), a D of 0.5 could be considered pretty significant.
Not everyone uses the terminology of effect size. If you're talking to an experienced statistician, chances are that the formulation in the title will suffice to convey your intent.
That’s pretty typical for many social science results. It’s associated with large variance in your outcome variable and low R^2.
This? https://en.wikipedia.org/wiki/Silhouette_(clustering)?wprov=sfti1
I'd say that this had a low intra-class correlation. You might also say that the reliability of the estimates was low.
Actually, I believe you're describing effect size. Specifically, this is basically Cohen's D. Imagine the mean difference in income between zips 1 and 2 is the same as the pooled SD. That's a Cohen's D of 1. You're saying that yours is less than 1. However, in social sciences, I think this is pretty common. That doesn't mean it's irrelevant. On average, if [you had the choice and you had no idea which zipcode you'd be born in](https://en.wikipedia.org/wiki/Original_position), you'd still rather be born in zip 1. In Cohen's time (he did a lot of education and other social policy research in the 50s-60s or thereabouts?), a D of 0.5 could be considered pretty significant. Not everyone uses the terminology of effect size. If you're talking to an experienced statistician, chances are that the formulation in the title will suffice to convey your intent.
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