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WIREs Comp Stat

Using simulation‐based inference for learning introductory statistics

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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

Simulation‐based estimate of p‐value for one proportion.
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Left panel displays observed sample data. Right panel displays shuffled results. Dotplot will accumulate shuffled statistics (e.g., mean absolute deviation).
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Result of card shuffling into groups to simulate random assignment process with fixed row and column totals. Blue cards indicate successes and green cards indicate failures. Dark bars at tops of cars indicate original group A membership. Number of successes randomly assigned to group A is tallied.
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Computational Intensive Statistical Methods > Bootstrap and Resampling
Modeling and Simulation > Simulation Models

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