Lee and his team are interested in the biases and misconceptions biomedical scientists have about statistics. What are the causes and contextual factors that predict these misconceptions? Their work extends Metaknowledge to the domain of scientific training and statistical training. The online platform they will utilize (Open Response Concept Testing, ORCT) identifies, categorizes, and measures misconceptions behind problem solving errors. Pinpointing the factors driving the misconceptions will allow them to determine the feasibility of scientists embracing big data.
Statistical Thinking: a scalable, online platform for identifying working scientists’ misconceptions
In This Section:
- Current Projects
- Aesthetics of Explanation
- Big Questions
- Cognitive and Evolutionary Foundations of Science
- Great Scientists
- Hidden Models
- Idea Generativity
- Levels of Description
- Lives of Concepts
- Machine Science
- Optimal Matching
- Peer Review
- Representations of Knowledge
- Schools of Thought
- Tradition and Innovation
- The Zeitgeist of Science
- Disambiguation Working Group
- Social Sciences Distinguished Lecture Series
- D.E.E.P: Discovering the Extent of Estimable Prediction in Science and Technology
- Social MIND project will build AI models to explain, predict and influence the social world
- New Research
- Our Funders
- Cloud Kotta
News
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Journal of Social Computing Launch
December 17, 2020
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Knowledge Lab team wins the IRIS Researcher Award
February 26, 2020
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Analysis of Wikipedia finds politically polarized teams produce better work
March 4, 2019
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Bigger teams aren’t always better in science and tech
February 13, 2019
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Postdoctoral Position in the Science of Teams and Innovation
August 23, 2018
Connect: