There remains a great deal of uncertainty about uncertainty estimation in hydrological modeling. Given that hydrology is still a subject limited by the available measurement techniques, it does not appear that the issue of epistemic error in hydrological data will go away for the foreseeable future, and it may be necessary to find a way to allow for robust model conditioning and more subjective treatments of potential epistemic errors in prediction. In this paper an attempt is made to analyze how this is the result of the epistemic uncertainties inherent in the hydrological modeling process and their impact on model conditioning and hypothesis testing. Some ideas are proposed about how to deal with assessing the information in hydrological data and how it might influence model conditioning based on hydrological reasoning, with an application to rainfall-runoff modeling of a catchment in northern England, where inconsistent data for some events can introduce disinformation into the model conditioning process. A methodology is presented to make an assessment of the relative information content of calibration data before running a model that can then inform the evaluation of model runs and resulting prediction uncertainties.