It is often too complex to use, and sometimes impossible to obtain, an actual model in simulation or command field . To handle a system in practice, a simplification of the real model is then necessary. This simplification goes through some hypotheses made on the system or the modeling approach. In this paper, we deal with all models that can be expressed by real-valued variables involved in analytical relations and depending on parameters. We propose a method that qualifies the simplification validity by verifying a quality threshold on the hypothesis relevance. This method, based on interval analysis, can check the acceptance of the hypothesis in a full range of the whole model space, and can give bounds to the quality threshold and to the model parameters.% to keep the simplified model acceptable. Our approach is experimentally validated on a robotic application.