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Given the high level of integration between methods and data it is quite hard to identify exactly what is generalizable.
“Thinking about Thinking” – How problem solving evolved in nature, how the mechanics of our brains work, and the psychological biases that can emerge when we think. “Philosophy, Science, and Problem Solving” – How humans have historically approached problem solving, from ancient times to the present. “Approaching Problems in the Natural Sciences” – How people in the natural sciences deconstruct problems. “Statistics and Problem Solving” – How statistics can be used to evaluate problems and think critically. “Approaching Problems in the Humanities” – How people in the social sciences and humanities deconstruct problems. “Evaluating the Anthropocene” – How to evaluate the problems of the Anthropocene.
This newsletter introduces the Problem Solving Model.
Mathematics, logic, theory, abstraction, and generalizable concepts play a large role in our research.
While we consider everything that could help solve the scientific problem, we easily discount concepts that have little practical validity, irrespective of their mathematical complexity or appeal.
Instead, an ever smaller number of statisticians work directly on emerging problems, new data structures, and data.
The problem is fundamental and obvious: there are very few statisticians working on data because the academic reward system and job market favor theoretical statistics. On this webpage we have tried to provide details about our research groups's solution for the future.For more information we invite you to read carefully, explore the various links, and have a look at our papers, software, and training materials.The second course of the specialization EVALUATING PROBLEMS will show you how humans think and how to utilize different disciplinary approaches to tackle problems more effectively.Our philosopy is to start with exciting new scientific problems, identify, and carefully tune statistical analytic approaches designed to solve them.Simple, easy to explain solutions are often the best, while other times subtler approaches are necessary.As its name implies, this model is the road map to follow to solve problems. a) When the process isn't doing what it is supposed to and people don't know why.b) When things keep going wrong no matter how hard everyone tries.machine learning with variability quantification), computational advances, visualization, and Structural Principal Component Analysis.For more details you can explore the Statistical methods submenu.We found the problem forward approach to be much more reasonable, satisfying, and honest.This approach exposes our students and collaborators to problems, data, and statistical analytic thinking much earlier in the process, giving us the chance to make an impact when it matters.