BioEssays · 2003 · 534 citations · 61 references
EducationData ScienceGenomicsNature Of SciencePhilosophy Of ScienceData AnalysisHypothesis FormationKnowledge DiscoveryScientific DiscoveryScientific KnowledgeScientific ProgressData-driven Scientific DiscoveryScientific Data AnalysisScientific HypothesesScientific Knowledge ConstructionTechnology Discovery
In some quarters, hypothesis‑driven methods are viewed as the sole reliable means of scientific progress, while data‑driven advances and technology are dismissed as marginal or irrelevant. The authors argue that data‑ and technology‑driven programmes are not alternatives to hypothesis‑led studies but are complementary and iterative partners. Computational methods of data analysis, which may be automated, provide the means of generating novel hypotheses, especially in the post‑genomic era. These computational methods enable the generation of novel hypotheses in data‑rich, hypothesis‑poor fields such as the post‑genomic era. BioEssays 26:99–105, 2004; © 2003 Wiley Periodicals, Inc.
Abstract 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. BioEssays 26:99–105, 2004. © 2003 Wiley Periodicals, Inc.
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