Wender Ben. Refining the Concept of Scientific Inference When Working with Big Data

National Academy of Sciences, 2017. — 101 p.The concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow (FTC, 2016; NITRD/NCO, 2016). Big data is considered herein as data sets whose heterogeneity, complexity, and size—typically measured in terabytes or petabytes—exceed the capability of traditional approaches to data processing, storage, and analysis. Using big data analytics to identify complex patterns hidden inside volumes of data that have never been combined could accelerate the rate of scientific discovery and lead to the development of beneficial technologies and products. For example, an analysis of big data combined from a patient’s electronic health records (EHRs), environmental exposure, activities, and genetic and proteomic information is expected to help guide the development of personalized medicine. However, producing actionable scientific knowledge from such large, complex data sets requires statistical models that produce reliable inferences (NRC, 2013). Without careful consideration of the suitability of both available data and the statistical models applied, analysis of big data may result in misleading correlations and false discoveries, which can potentially undermine confidence in scientific research if the results are not reproducible. Thus, while researchers have made significant progress in developing techniques to analyze big data, the ambitious goal of inference remains a critical challenge.