= Solution
The <Student t random-effect model> is useful when most studies are comparable but occasional genuine departures are more frequent than a <normal distribution> hierarchy allows. Its heavier tails permit a study effect far from $\mu$ without forcing a large common <between-study heterogeneity> scale on every study. In the <Gaussian scale mixture> representation, a small study-specific $\lambda_j$ lowers its <precision parameter> and weakens its shrinkage.
\b[Use this as robust partial pooling when occasional atypical effects are plausible.] Known systematic population or design differences should still be modeled explicitly; a heavy tail cannot identify or correct <within-study bias> by itself.
Back to article page
