Statistical thinking for the era of big data and artificial intelligence: toward understanding sustainability trends and issues for the future society

Abstract

This paper aims to characterize the thinking processes needed for the practice of statistics with big data and data analytics platforms driven by artificial intelligence. Such thinking has evolved from a traditional question-then-answer analysis to a more creative approach, which starts with data-first answers from examining opportunistic data, and then works backward to find the questions that should have been asked. Concerned with the rare use of the latter approach at school level, the authors developed a six-phase framework for the statistical thinking needed for the practice of statistics in a technologically- and data-rich world, in the context of the APEC-funded project “Inclusive Mathematics for Sustainability in a Digital Economy” (InMside). An exemplar related to a sustainability issue is provided to briefly illustrate the framework implementation.