This preprint develops a response-theory approach to tuning model parameters so that simulated statistics agree with observations. The generalized fluctuation-dissipation theorem relates small changes in parameters to changes in statistics of chosen observables. Score estimates support the calculation of these sensitivities, which can then be used in Newton-type or regularized least-squares updates. The April 2026 revision tests sensitivities of drift and diffusion parameters on analytically tractable processes and stochastic Lorenz–96 models. Readers get a principled way to construct statistical parameter sensitivities without adjoint models or separate perturbation ensembles, together with the limits imposed by linear response.