It is considered in some quarters that hypothesis-driven methods are the only valuable, reliable or significant means of scientific advance. Data-driven or 'inductive' advances in scientific knowledge are then seen as marginal, irrelevant, insecure or wrong-headed, while the development of technology--which is not of itself 'hypothesis-led' (beyond the recognition that such tools might be of value)--must be seen as equally irrelevant to the hypothetico-deductive scientific agenda. We argue here that data- and technology-driven programmes are not alternatives to hypothesis-led studies in scientific knowledge discovery but are complementary and iterative partners with them. Many fields are data-rich but hypothesis-poor. Here, computational methods of data analysis, which may be automated, provide the means of generating novel hypotheses, especially in the post-genomic era.
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