Daniela Witten: The Statistical Challenges of Increased Data

A University of Washington professor discusses the consequences of more data and its impact on biomedical sciences.
Daniela Witten’s talk at the 2018 Women in Data Science (WiDS) conference centered around a question: Within the context of interdisciplinary data science, what is the number one thing that rigorous statistical training brings to the table? To Witten, associate professor of statistics and biostatistics at the University of Washington, the answer is easy: a thoughtfully designed experiment that addresses deeper questions about data and biology.
“If we sequence your DNA and we find out which de novo mutations you have, how can we make sense of those? How do we know which of those are likely to be harmful and increase your risk for disease or explain a disease that you already have?” she asked.
She posited that the answer is not to collect more data, but rather to find out why these mutations matter, using statistics.
“We’re going to need to fill in the gaps through statistical modeling in order to be able to not only make use of the data we have, but also answer scientific questions that can’t be answered by way of data alone.”