Mathematical models incorporating random forcing, and the resulting stochastic differential equations (SDEs), are becoming increasingly important. However general principles and techniques for their robust and efficient numerical approximation are a very long way behind the corresponding ODE theory. In both cases the idea of adaptivity, that is using varying timesteps to improve convergence, is a key element. In this talk I will describe an approach based upon (low-order) Milstein-type methods using multiple error-controls. The idea is to monitor various terms in the truncation error, both deterministic and stochastic, and then to construct an algorithm that is robust enough to work efficiently in the presence of deterministic/diffusion-dominated regimes and differing accuracy requirements. Such an approach also has other benefits, such as improved numerical stability properties. No knowledge of stochastic calculus will be assumed.