The application of interval-valued statistical models is often hindered by the rapid growth in imprecision that occurs when intervals are propagated through models. Is this deficiency inherent in the models? If so, what is the underlying cause of imprecision in mathematical terms? What kind of additional information can be incorporated to make the bounds tighter? The present paper gives an account of the source of this imprecision that prevents interval-valued statistical models from being widely applied. Firstly, the mathematical approach to building interval-valued models (discrete and continuous) is delineated. Secondly, a degree of imprecision is demonstrated on some simple reliability models. Thirdly, the root mathematical cause of sizeable imprecision is elucidated and, finally, a method of making the intervals tighter is described. A number of examples are given throughout the paper.