In most cases, it is hard to have a good understanding of all possible (probablistic) consequences of investment decisions. I propose an approach in which decision makers first categorize projects based on similarity as perceived from small samples, then get access to aggregate statistics about the effect of the investment in the various categories of projects, and finally make investment decisions in randomly drawn projects based on these aggregate statistics. I compare the resulting outcome to that arising in a benchmark Bayesian model and highlight the mistakes of the selection bias type that such heuristics induce. I also apply the approach to multi-player contexts so as to shed light on the so called no trade theorem. 