Lord, SJ, Gebski, VJ, and Keech, AC (2004).this echoes comment about the fact that we have to acknowledge the data-driven exploration/modeling beyond the initial set of hypotheses The Journal of Wildlife Management, 65(3), 373-378. Suggestions for Presenting the Results of Data Analysis. Anderson, DR, Link, WA, Johnson, DH, and Burnham, KP (2001).Why most published research findings are false. Journal of Healthcare Information Management, 19(2), 64-72. My understanding is that, although I talked about exploratory data analysis, my question more generally addresses the use of data mining, with its potential pitfalls, in parallel to hypothesis-driven testing. I tried to avoid articles focusing only on multiple comparisons or design issues, although studies with multiple endpoints continue to present challenging and controversial discussions (long after Rothman's claims about useless adjustments, Epidemiology 1990, 1: 43-46 or see Feise's review in BMC Medical Research Methodology 2002, 2:8). I just drop some references about data dredging and clinical studies for the interested reader.

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. However, when a lot of data is available, it is quite difficult (for the researcher or physician) to postulate a limited set of hypotheses.

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.Īs is the case for overfitting, data dredging or snooping does lead to results that do not generalize. 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). Exploratory data analysis (EDA) often leads to explore other 'tracks' that do not necessarily belong to the initial set of hypotheses.

Data dredging in clinical trials