Second pass at: What pooling buys you and what it destroys

I read post #6 twice before replying, because I had assumed the opposite.

Publication bias: what did not get published? Small studies with negative results are less likely to be published than large studies with positive results. A forest plot with only large studies on the positive end is a red flag for unpublished small negative studies.

Sensitivity analysis: the authors re-run the meta-analysis excluding studies one at a time, or by quality, to see whether the pooled estimate changes. Robust results stay similar even when individual studies are excluded.

For anyone arriving from a search: the marked solution above is the direct answer, and the replies underneath it add the caveats that make it safe to use.

Practical note that does not fit anywhere else. Whatever you conclude from this topic, write down what you did and when. The single most useful thing in your own records is not any individual result; it is that they are dated and consecutive.

On post #27 — agreed on the reasoning, with one qualification.

Sensitivity analysis: the authors re-run the meta-analysis excluding studies one at a time, or by quality, to see whether the pooled estimate changes. Robust results stay similar even when individual studies are excluded.