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Using simulation‐based inference for learning introductory statistics
Author(s) -
Rossman Allan J.,
Chance Beth L.
Publication year - 2014
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1302
Subject(s) - resampling , statistical inference , computer science , curriculum , inference , feature (linguistics) , statistical analysis , statistical hypothesis testing , key (lock) , component (thermodynamics) , machine learning , summary statistics , fiducial inference , statistics education , artificial intelligence , mathematics education , data science , statistics , frequentist inference , mathematics , psychology , bayesian inference , pedagogy , linguistics , philosophy , physics , computer security , thermodynamics , bayesian probability
Recent curriculum development projects emphasize teaching simulation and randomization‐based statistical inference as a prominent feature in introductory statistics courses. We describe the goals, distinctive features, and examples from some of these projects. Technology is a key component of these courses, so we mention desirable features of the various technology products used with this approach. We also discuss how student learning is being assessed in such courses, along with how the curriculum effort itself is being evaluated. We also touch on some challenges that we have encountered with teaching these courses, both from a student and a faculty viewpoint. WIREs Comput Stat 2014, 6:211–221. doi: 10.1002/wics.1302 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bootstrap and Resampling Statistical Models > Simulation Models