Yes, two extra notes on this: 1. Weakly informative priors can be good to regularize and stabilize inference in scenarios with a low amount of data. 2. In cas

Yes, two extra notes on this: 1. Weakly informative priors can be good to regularize and stabilize inference in scenarios with a low amount of data. 2. In case of the review scenario presented by the OP, a hierarchical model could be even better as it would achieve regularization (and shrinking) by borrowing information across different sellers. In other words, one would learn the overall distribution of seller reliability at the same time as individual ones. This has two advantages: a) Just one (hyper)prior for the overall distribution, but no priors needed at seller level and b) seller predictions are pulled towards the mean in a principled way. Most scientific publications that find unusually large effects coming from some association turn out to be false. If they used shrinking (2), they would avoid excessively optimistic predictions. See: https://en.wikipedia.org/wiki/Stein%27s_example https://en.wikipedia.org/wiki/Stein%27s_example