  Conditional stability estimates allow us to characterize the degree of
ill-posedness of many inverse problems, but without further assumptions they
are not sufficient for the stable solution in the presence of data
perturbations. We here consider the stable solution of nonlinear inverse
problems satisfying a conditional stability estimate by Tikhonov regularization
in Hilbert scales. Order optimal convergence rates are established for a-priori
and a-posteriori parameter choice strategies. The role of a hidden source
condition is investigated and the relation to previous results for
regularization in Hilbert scales is elaborated. The applicability of the
results is discussed for some model problems, and the theoretical results are
illustrated by numerical tests.
