I present a novel algorithm called Stochastic Outlier Selection (SOS). The SOS algorithm computes for each data point an outlier probability. These probabilities are more intuitive than the unbounded outlier scores computed by existing outlier-selection algorithms. I have evaluated SOS on a variety of real-world and synthetic datasets, and compared it to four state-of-the-art outlier-selection algorithms. The results show that SOS has a superior performance while being more robust to data perturbations and parameter settings.