  Conditional stability estimates require additional regularization for
obtaining stable approximate solutions if the validity area of such estimates
is not completely known. In this context, we consider ill-posed nonlinear
inverse problems in Hilbert scales satisfying conditional stability estimates
characterized by general concave index functions. For that case, we exploit
Tikhonov regularization and provide convergence and convergence rates of
regularized solutions for both deterministic and stochastic noise. We further
discuss a priori and a posteriori parameter choice rules and illustrate the
validity of our assumptions in different model and real world situations.
