1st Edition

The Learning and Teaching of Statistics and Probability A Perspective Rooted in Quantitative Reasoning and Conceptual Coherence

    164 Pages 33 B/W Illustrations
    by Routledge

    164 Pages 33 B/W Illustrations
    by Routledge

    Filled with practical learning activities to adopt within your classroom, The Learning and Teaching of Statistics and Probability places reasoning about quantities and quantification at the core of learning and teaching statistics. A companion website to this book is also available at https://neilhatfield.github.io/IMPACT_Statistics/, allowing readers to access a directory of resources – data collections and web-based applets – used in some of the instructional activities featured within this book.

    Through its presentation of conceptual analyses and resources for teaching with statistical data, the book’s five chapters establish key concepts and foundational ideas in statistics and probability, emphasizing the development of learner understanding and coherence, for example:

    • Individual cases and their attributes
    • Data collections, sub-collections, and relevant operations to quantify their attributes
    • Samples, population, and quantifying variation
    • Types of processes, meanings of randomness, and probability as a measure of stochastic tendency
    • Sampling distributions and statistical inference.

    This highly informative yet practical book is an indispensable resource for teachers of secondary school mathematics, mathematics subject leads, and mathematics and statistics educators within the wider field of education.

    Introduction;  1. Individual Cases, Attributes, and Data;  2. Collections of Cases, Attributes of Collections, and Measures of Such Attributes;  3. Samples, Populations, and Quantifying their Variation;  4. Processes, Randomness, and Probability;  5. Sampling Distributions and Statistical Inference;  Appendix;  Index


    Luis Saldanha is a professor of Didactics of Mathematics in the department of mathematics at l’Université du Québec à Montréal, Canada. His research focuses on mathematical thinking, specifically the development of students’ statistical reasoning in relation to their engagement with instruction designed to foster their understanding of statistical concepts.

    Neil J. Hatfield is an assistant research professor in the Department of Statistics at Pennsylvania State University, U.S.A. His main research interests focus on cognition related to the concept of distribution; the teaching of statistics, data science, and probability; and diversity, equity, and inclusion in STEM.

    Egan J. Chernoff is a professor of Mathematics Education at the University of Saskatchewan, Canada. His editorial affiliations include Statistics Education Research Journal; The Mathematics Enthusiast; Mathematical Thinking and Learning; Journal of Mathematical Behavior; Canadian Journal of Science, Mathematics, and Technology Education; and more.

    Caterina Primi is a full professor in Psicometria at the Faculty of Psychology at the University of Florence, Italy. She is an experienced teacher of graduate, post-graduate, and PhD level courses in statistics, research methods, and psychological testing.

    "This book definitely makes me think about the ways we normally talk about statistics with our students and our prospective teachers, and that perhaps there are alternative ways for us to introduce data, statistics, and inference than many of us do now. The authors are right-on with their path: Cases--> Collections--> Samples--> Distributions. I applaud that, and also their excellent "mini" and "integrating" activities that they have included throughout the book to get us all thinking and reasoning about quantities in statistics."

    J. Michael Shaughnessy, Professor Emeritus in Mathematics and Statistics at Portland State University.