We discuss the question of model identiability within the context of non-linear mixed eects models. Although there has been extensive research in the area of xed eects models, much less attention has been paid to random effects models. In this context we distinguish between theoretical identiability, in which dierent parameter values lead to non-identical probability distributions , structural identiability which concerns the algebraic properties of the structural model, and practical identiability, whereby the model may be theoretically identiable but the design of the experiment may make parameter estimation dicult and imprecise. We explore a number of pharmacokinetic models which are known to be non-identiable at an individual level but can become identiable at the population level if a number of specic assumptions on the probabilistic model hold. Essentially if the probabilistic models are different , even though the structural models are non-identiable, then they will lead to dierent likelihoods. The ndings are supported through simulations.