Determine if treatment effect is distributed equally across participants in a study or driven by a few participants This is perhaps a malformed question, but I'll give it a shot. Let's say I have a pre and post measure of some outcome variable and a dummy variable treatment that is 1 if participants are in a treatment condition and 0 if control.
Let's say I model the effect on the outcome variable simply with:
lm(outcome_post - outcome_pre ~ treatment)
Let's say the coefficient on treatment is 3.0 and is highly statistically significant. What I want to answer is: how evenly distributed is this treatment effect across participants? It could be the case that only a handful of participants benefits from the treatment, but they benefit to a significant degree.
Is there a test or procedure I can use to test whether treatment effects are evenly distributed in the sample?

This can be addressed by reframing your model - which is worth doing anyway.
Change scores like [outcome_post - outcome_pre] are not the recommended approach to model pre/post data. Instead, a better approach is to use the pre measurement as a covariate i.e. do lm(outcome_post ~ outcome_pre * treatment). The interaction term will then tell you what you want to know about how variable the treatment effects are.
This is an excellent thread that explains how to analyse pre-post designs in more detail, though it doesn't explicitly address your question about variability in treatment effects: Best practice when analysing pre-post treatment-control designs
