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.Our approach to research is "problem forward", as we start with and focus on the scientific problem.
c) When everyone believes that there is a problem to solve.
The first step in the model is to define the problem; it does not matter if it is late shipments, stock outs, computer downtime, typos, lost messages, or an agreed upon "red bead" that everyone keeps running into.
“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.
This is a ten-step model to guide you (and your team) through a structured problem solving process.
All too often, people jump from a problem to a solution.machine learning with variability quantification), computational advances, visualization, and Structural Principal Component Analysis.For more details you can explore the Statistical methods submenu.It advances your knowledge of your own field by teaching you to look at it in new ways.EVALUATING PROBLEMS is constructed in the following way: Week I.Not every application leads to a new method and many years of problem solving sometimes result in generalizable expertise.We call that the statistical method, which is an important component of our research.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.Given the high level of integration between methods and data it is quite hard to identify exactly what is generalizable.Here we provide a list of interesting generalizable concepts (a.k.a.