When there is a strong signal in a large dataset, many machine-learning algorithms will find it. On the other hand, when the effect is weak and the data is large, there are many ways to discover an effect that is in fact nothing more than noise. Robert Grossman shares best practices by exploring three case studies to make it a bit less likely that you will be accused of p-hacking.
The first case study concerns mutations in breast cancer and some of the complexities of understanding rare mutations and combinations of rare mutations. In the second case study, Robert dives into different methods for understanding whether there is an effect on the health of newborns when pregnant women are exposed to particulate matter (solid and liquid particles suspended in air). The third case study looks at a well-known published paper offering evidence for ESP. Robert extracts several techniques from these three case studies that have consistently proved useful and discusses how best these techniques can be used in practice.
Robert Grossman is a faculty member and the chief research informatics officer in the Biological Sciences Division of the University of Chicago. Robert is the director of the Center for Data Intensive Science (CDIS) and a senior fellow at both the Computation Institute (CI) and the Institute for Genomics and Systems Biology (IGSB). He is also the founder and a partner of the Open Data Group, which specializes in building predictive models over big data. Robert has led the development of open source software tools for analyzing big data (Augustus), distributed computing (Sector), and high-performance networking (UDT). In 1996, he founded Magnify, Inc., which provides data-mining solutions to the insurance industry and was sold to ChoicePoint in 2005. He is also the chair of the Open Cloud Consortium, a not-for-profit that supports the research community by operating cloud infrastructure, such as the Open Science Data Cloud. He blogs occasionally about big data, data science, and data engineering at Rgrossman.com.
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