The presence of measurement and misclassification errors in data sets (present in most data sets, in my experience) are often wrongfully considered relatively unimportant. The Data Are Probably Error Prone, Incomplete, and Clustered 2 To avoid such errors, studies with an explanatory aim may benefit from applying causal inference methodology.

For instance, for a nonexperimental before-after study, a change in the health for some individuals over time is easily mistaken as evidence for the effectiveness of a particular curative treatment, which may just be caused by regression to the mean. This type of data is subject to factors that hamper our ability to distinguish between true causes of outcomes and mere correlations. Conversely, for many health-related research questions, nonexperimental data are the only viable source of information. 1 If the ultimate aim is to explain, the ideal design is often an experiment (eg, a randomized controlled trial). The subsequent planning of the collection of useful data and formulating adequate statistical analysis often becomes easier once it is clarified whether the ultimate aim is to predict, explain, or describe.

As the starting point of all scientific endeavors, it is incontrovertibly important to clearly define the research questions and aims.

Data-dredging