The problem of non-random sample selectivity often occurs in practice in many different fields. The classical estimators introduced by Heckman (1979) are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. In this paper we develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of the Heckman’s two-stage estimator, and demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the assumed model. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and compare the performance of the classical and robust methods in a Monte Carlo simulation study.