Introduction to Statistical Investigations
Nathan Tintle, Beth Chance, George Cobb, Allan Rossman, Soma Roy, Todd Swanson, and Jill VanderStoep
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Preface | Table of Contents | Chapter 1 | Sample Materials | Project | Conference Presentations | Blog | Instructor Resources


Winner of MAA's 2018
 Daniel Solow Author's Award

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Introduction to Statistical Investigations is an introductory statistics text developed at Hope College, Dordt College, Cal Poly, and Mount Holyoke and the first edition is available from John Wiley & Sons. (Text Website at Wiley) 

The text differs from traditional texts in both content and pedagogy.  Statistical inference is introduced using simulation-based methods in Chapter 1.  This allows students to intuitively understand the process of inference from the very beginning of their course. Concepts of statistical inference are then explored for the entire text instead of only the last half of many traditional texts. Students are actively engaged in the material through numerous activities in which they flip coins, draw cards, collect data, do computer simulations and run experiments.  Computer simulations are done with freely available applets.  (See Efficacy Flyer for information on student performance.)

We are currently working on an assessment initiative of our curriculum, other randomization curriculums, and more traditional introductory statistics curriculums. This assessment will include pre- and post- concepts and attitudes assessments as well as common exam questions. We are looking for participants and non-users of our curriculum are especially needed. Our goals are to determine what works, what doesn’t, comparisons by institution, instructor, style, etc.  If you participate you will receive Individualized instructor reports to learn about your own students outcomes.

For more information about our curriculum or participation in our assessment efforts described above contact Nathan Tintle at

Workshops since 2014
Papers written by the authors about our curriculum
              This material is based upon work supported from the National Science Foundation under (Grant DUE-1140629) and (Grant DUE-1323210)
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.