In the traditional scientific method, a hypothesis is followed by extensive and tedious experiments that were used for proving the theory. In science education, in fact, we pay more emphasis on the procedural experimental part rather than on the creative task of identifying a reasonable hypothesis. This non-creative focus has notably discouraged learners embarking on science education, especially when better options are available (Example 1, Example 2). In the presence of emerging Big Data concepts and technologies, it appears that the traditional scientific method is challenged (Example 1). Large volumes of diverse data flowing around us, especially in electronic formats, can be integrated and used for the purpose of proving hypotheses rather than relying heavily on experimental methods. In other words, in many cases, we do not have to collect data by conducting experiments as various forms of related data are available in accessible and searchable formats. Think about the validity of a hypothesis if one or two other individuals from some other corners of the world come up with similar hypotheses independent of each other. Consequently, the focus of science education should be shifted to the creative task of identifying useful hypotheses or ideas ahead of conducting experimental procedures, soon to be a thing of the past. It is likely to attract the interest of novelty-seeking human beings on once mundane science education.
https://www.wired.com/2008/06/pb-theory/
https://theconversation.com/three-ways-to-boost-science-performance-in-australian-schools-69770
https://theconversation.com/what-to-say-if-your-child-asks-whats-the-point-of-maths-69628
Experimental design is an art as well as a science, and so is coming up with hypothesis and building a framework for testing them. The availability of big data does not mean that we can make causal inferences from them without models. There are several areas where randomized controlled trials are still the gold standard to avoid selection bias, confounding, etc.
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