Picture is sometimes worth thousand words, so let me share one with you. Below you can see illustration that comes from Bradley Efron's (1977) paper [*Stein's paradox in statistics*][1]. As you can see, what Stein's estimator does, is that it moves each of the values closer to the grand average. By shrinkage we mean *moving the values toward average*, or *towards zero* in some cases like regularized regression that shrinks the parameters towards zero.

[![Illustration of Stein estimator from Efron (1977)][2]][2]


  [1]: http://statweb.stanford.edu/~ckirby/brad/other/Article1977.pdf
  [2]: https://i.sstatic.net/FXtoT.png