There have been intensive debates on the role of aid in promoting economic development in developing countries by using cross-country analyses. Cross-country regression assuming linear relationship between aid and growth and without taking into heterogeneity of countries would produce biased estimates. To correct this, in this paper we investigate the relationship between foreign aid and growth using recently developed sample splitting methods that allow us to simultaneously uncover evidence for the existence of heterogeneity and nonlinearity. We also address model uncertainty in the context of these methods. We find some evidence that aid may have heterogeneous effects on growth across two growth regimes defined by ethno-linguistic fractionalization. However, when we account for model uncertainty, we find no evidence to suggest that the relationship between aid and growth is nonlinear. In fact, our results suggest that the partial effect of aid on growth is likely to be weakly negative. In this sense, our findings suggest that aid is potentially counterproductive to growth with outcomes not meeting the expectations of donors. However due to the data comparability problem and inherent econometric problem in cross-country regression, more detailed country case studies are needed to evaluate the impact of aid at the country level and even project level. This can really answer the question that under what conditions what type of aid helped growth and poverty reduction in developing countries. The methodology developed in this paper can be used to identify typologies on other outcome variables, such as those included in the Millennium Development Goals.