Big data and its epistemology

Research output: Contribution to journalArticlepeer-review

61 Scopus citations

Abstract

The article considers whether Big Data, in the form of data-driven science, will enable the discovery, or appraisal, of universal scientific theories, instrumentalist tools, or inductive inferences. It points out, initially, that such aspirations are similar to the now-discredited inductivist approach to science. On the positive side, Big Data may permit larger sample sizes, cheaper and more extensive testing of theories, and the continuous assessment of theories. On the negative side, data-driven science encourages passive data collection, as opposed to experimentation and testing, and hornswoggling ("unsound statistical fiddling"). The roles of theory and data in inductive algorithms, statistical modeling, and scientific discoveries are analyzed, and it is argued that theory is needed at every turn. Data-driven science is a chimera.

Original languageEnglish (US)
Pages (from-to)651-661
Number of pages11
JournalJournal of the Association for Information Science and Technology
Volume66
Issue number4
DOIs
StatePublished - Apr 1 2015

Keywords

  • knowledge

ASJC Scopus subject areas

  • Information Systems and Management
  • Library and Information Sciences
  • Computer Networks and Communications
  • Information Systems

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