A fundamental property of mathematical models in ecology and epidemiology is sensitivity of model outcomes to the precise equations used. Indeed, the ‘exact’ mathematical formulation of model functions is often unknown; however the use of slightly different functions fitting well the same dataset may give significantly different predictions. In this case, the model is said to be ‘structurally sensitive’ and its implementation may be grossly misleading. Even for a purely deterministic model the uncertainty in model functions (e.g. uncertainty in formulation of growth rates, functional responses, mortality terms, etc) carries through the uncertainty of model predictions and thus it can be a serious obstacle in ecological modelling, especially when making a decision in ecological management based on model prediction. In this talk, I will firstly discuss how the uncertainty in predictions using biological models with structural sensitivity can be quantified and estimated. In the second part of the talk, I will revisit the fundamental question of how empirical data (including model-guided data collection process) should be implemented for enhancing predictability of ecological models with structural sensitivity.