Exploratory data analysis (EDA) often leads to explore other "tracks" that do not necessarily belong to the initial set of hypotheses. I face such a situation in the case of studies with a limited sample size and a lot of data gathered through different questionnaires (socio-demographics data, neuropsychological or medical scales -- e.g., mental or physical functioning, depression/anxiety level, symptoms checklist). It happens that EDA helps to highlight some unexpected relationships ("unexpected" meaning that they were not included in the initial analysis plan) that translates into additional questions/hypothesis.
As is the case for overfitting, data dredging or snooping does lead to results that do not generalize. However, when a lot of data is available, it is quite difficult (for the researcher or physician) to postulate a limited set of hypotheses.
I would like to know if there are well-acknowledged methods, recommendations, or rules of thumb that may help to delineate EDA in the case of small-sample studies.