This Title All WIREs
How to cite this WIREs title:
WIREs Comp Stat

Using simulation‐based inference for learning introductory statistics

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

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
Simulation‐based estimate of p‐value for one proportion.
[ Normal View | Magnified View ]
Left panel displays observed sample data. Right panel displays shuffled results. Dotplot will accumulate shuffled statistics (e.g., mean absolute deviation).
[ Normal View | Magnified View ]
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.
[ Normal View | Magnified View ]

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts