Table 2 Pitfalls/problems of big data

Data quality

Completeness of data

 Informative missing data

 Selective

 Representative for the problem at hand

Robustness of data

Correctness of data

Relevance of data

Representative data for the group at hand

Granularity of data

Definitions of data labels

 Not uniform

 Not precise

 Not clear

 Dichotomic/categorized

Information overload

Too much datapoints

Too much variables

 Known

 Unknown

Literature overload

 Fast evolution

 Overspecialized

Publication/Reporting bias

Non-reporting of data

Reporting of non-prespecified analyses

Framing

Unplanned sub-analysis and post-hoc analysis

Inappropriate statistical or methodological approach

Confusing causal and associative interpretations

Confusing statistical vs clinical relevance (the p-value problem)