Beyond Calculations: A Course in Statistical Thinking.

Statisticians are in general agreement that there are flaws in how science is currently practiced; there is less agreement in how to make repairs. Our prescription for a Post-p < 0.05 Era is to develop and teach courses that expand our view of what constitutes the domain of statistics and thereby br...

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Publicado en:American Statistician Vol. 73; pp. 392 - 402
Autores principales: Steel, E. Ashley, Liermann, Martin, Guttorp, Peter
Formato: Artículo
Publicado: Taylor & Francis Ltd Mar2019 Supplement
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        10.1080/00031305.2018.1505657
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        atl: Beyond Calculations: A Course in Statistical Thinking.
      aug:
        au:
          Steel, E. Ashley
          Liermann, Martin
          Guttorp, Peter
        affil:
          PNW Research Lab, USDA Science Center, SeattleWA
          Northwest Fisheries Science Center, Seattle, WA
          Norwegian Computing Center, Oslo, Norway
          Professor Emeritus, University of Washington, Seattle, WA
      su:
        Numerical calculations
        Probabilistic inference
        Inferential statistics
        Simulation methods & models
        Statistics education
        P-value (Statistics)
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        subj:
          Numerical calculations
          Probabilistic inference
          Inferential statistics
          Simulation methods & models
          Statistics education
          P-value (Statistics)
      keyword:
        p-Values
        Probabilistic thinking
        Simulation-based testing
        Statistical intuition Statistics education
        p-Values
        Probabilistic thinking
        Simulation-based testing
        Statistical intuition Statistics education
      ab: Statisticians are in general agreement that there are flaws in how science is currently practiced; there is less agreement in how to make repairs. Our prescription for a Post-p < 0.05 Era is to develop and teach courses that expand our view of what constitutes the domain of statistics and thereby bridge undergraduate statistics coursework and the graduate student experience of applying statistics in research. Such courses can speed up the process of gaining statistical wisdom by giving students insight into the human propensity to make statistical errors, the meaning of a single test within a research project, ways in which p-values work and don't work as expected, the role of statistics in the lifecycle of science, and best practices for statistical communication. The course we have developed follows the story of how we use data to understand the world, leveraging simulation-based approaches to perform customized analyses and evaluate the behavior of statistical procedures. We provide ideas for expanding beyond the traditional classroom, two example activities, and a course syllabus as well as the set of statistical best practices for creating and consuming scientific information that we develop during the course.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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