Episode #17: Big Data and Good Science

Scientists have long debated the best methods to achieve sound findings. In recent decades, hypothesis-driven frameworks have been enshrined in textbooks and school courses, with iterative and inductive approaches often taking a back seat. However, the advent of big data poses a challenge to the established dogma, as large data sets often require broad collaborations and make traditional hypothesis-driven approaches less tractable. For this episode of BioScience Talks, we spoke with Michigan State University professors Kendra Cheruvelil, Georgina Montgomery, Kevin Elliott, and Patricia Soranno. Their interdisciplinary work highlights the changing scientific landscape, in which large data sets and new computational methods encourage a more iterative approach to science.

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