Extracting Evidence from Biological Data: Multiple Disciplines Get In on the Act

Saturday, 14 February 2015: 3:00 PM-4:30 PM
Room LL21D (San Jose Convention Center)
Biologists often depend on statistics for the interpretation of data, in fields ranging from evolutionary biology to “big data”–driven fields such as genomics and proteomics. But statistical results in biology have been getting some bad publicity lately in connection with non-reproducibility, and there is growing awareness that sole reliance on the workhorse of conventional statistical analysis -- the p-value -- can lead us to misinterpret the strength of evidence in some settings. What gets less media attention is the emergence of alternative approaches to inference in biology, which can change the conclusions we draw when analyzing complex data. In addition to important ongoing work within statistics itself, many interesting new ideas are coming out of other disciplines, including physics, computer science, measurement theory, and philosophy of science. But because the methods are being developed within disciplines that tend not to interact with one another, the emergence of patterns across disciplines can be hard to spot. This symposium considers the fundamental problems of inference and measurement as they apply to biological and biomedical research, focusing on emerging work both in statistical and nonstatistical fields and the connections among developments from multiple disciplinary perspectives, including the perspective of science journalism and the public interest in ensuring that the findings that make the nightly news are based on sound statistical methodology.
Veronica Vieland, The Research Institute at Nationwide Children's Hospital
Rahul Singh, San Francisco State University and Center for Discovery and Innovation in Parasitic Diseases, University of California
Computational Frameworks for Evidence Combination
Veronica Vieland, The Research Institute at Nationwide Children's Hospital
Towards a New Information-Dynamic Framework for Measuring Evidence in Biology
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