We propose a stochastic first-order trust-region method with inexact function and gradient evaluations for solving finite-sum minimization problems. At each iteration, the function and the gradient are approximated by sampling. The sample size in gradient approximations is smaller than the sample size in function approximations and the latter is determined using a deterministic rule inspired by the inexact restoration method, which allows the decrease of the sample size at some iterations. The trust-region step is then either accepted or rejected using a suitable merit function, which combines the function estimate with a measure of accuracy in the evaluation. We show that the proposed method eventually reaches full precision in evaluating the objective function and we provide a worst-case complexity result on the number of iterations required to achieve full precision. We validate the proposed algorithm on nonconvex binary classification problems showing good performance in terms of cost and accuracy and the important feature that a burdensome tuning of the parameters involved is not required.