The BioScience Talks podcast features discussions of topical issues related to the biological sciences.
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 Kevin Elliott, Kendra Cheruvelil, Georgina Montgomery, and Patricia Soranno. Their interdisciplinary work, described in the journal BioScience , highlights the changing scientific landscape, in which large data sets and new computational methods encourage a more iterative approach to science. However, the authors are quick to note that despite the newness of the technology, the reinvigorated approaches are anything but: The debate over iterative and hypothesis-driven science has raged all the way back to Darwin, and beyond.
To hear the whole discussion, visit this link for this latest episode of the Bioscience Talks podcast.
###
BioScience