The Science of Statistics Recommendations

Key Recommendations:

1.  Focus on developing central statistical ideas that relate to the statistical process of formulate statistical questions, collect/consider data, analyze data, and make inferences rather than on presenting set of tools and procedures.

2. Promote classroom discourse that includes statistical arguments and sustained exchanges that focus on significant statistical ideas that pertain to the formulation of statistical questions, collecting/considering data, analyzing data, and making inferences.

3. Use assessment to learn what students know and to monitor the development of their statistical learning as well as to evaluate instructional plans and progress.

4. Integrate the use of appropriate technological tools that allow students to test their conjectures, explore and analyze data, develop their statistical reasoning and thinking, and connect the statistical process with appropriate inferences.

5. Use real and motivating datasets to engage students in making and testing conjectures that come from within and outside the classroom.

6. Use classroom activities to support the development of students’ reasoning about the statistical process.

7. Focus on variability throughout the statistical process by anticipating variability in the question formulation, acknowledging variability in the data collection process developing ideas for reduction of this variability, accounting for variability in analysis by displaying it and measuring it during analysis, and allowing for variability when making inferences about the statistical process.

Research Base:

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Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D. (2020). Pre-K–12 guidelines for assessment and instruction in statistics education II (GAISE II) (2nd ed.). American Statistical Association.

Ben-Zvi, D., Aridor, K., Makar, K., and Bakker, A. (2012). Students/ emergent articulations of uncertainty while making informal statistical inferences. International Journal of Mathematics Education. 

Ben-Zvi, D. (2006). Scaffolding students’ informal inference and argumentation. In A. Rossman and B. Chance (Eds.) Proceedings of the seventh International Conference on Teaching of Statistics, Salvador, Bahia, Brazil, July 2–7, 2006. Voorburg, The Netherlands: International Statistical Institute. www.stat.auckland.ac.nz/iase/publications/17/2D1_BENZ.pdf. 

Ben-Zvi, D. (2000). Toward understanding the role of technological tools in statistical learning. Mathematical Thinking and Learning, 2:1–2, 127–155.

Ben-Zvi, B., and Garfield, J. (Eds.). (2004). The Challenge of Developing Statistical Literacy, Reasoning, and Thinking. Dordrecht, The Netherlands: Kluwer Academic Publishers.

Ben-Zvi, D., and Friedlander, A. (1997b). Statistical thinking in a technological environment. In J. Garfield and G. Burrill (Eds.) Research on the role of technology in teaching and learning statistics (pp. 45–55). Voorburg, The Netherlands: International Statistical Institute.

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Casey, S., and Wasserman, N. (2015). Teachers’ knowledge about informal line of best fit. Statistics Education Research Journal, 14(1), 8-35.

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Conway IV, B., Gary Martin, W., Strutchens, M., Kraska, M., & Huang, H. (2019). The statistical reasoning learning environment: A comparison of students’ statistical reasoning ability. Journal of Statistics Education, 27(3), 171-187.

delMas, R., Garfield, J., Ooms, A., and Chance, B. (2007). Assessing students’ conceptual understanding after a first course in statistics. Statistics Education Research Journal, 6(2):28–58. 

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Shaughnessy, J. M., Ciancetta, M., and Canada, D. (2004). Types of students reasoning on sampling tasks. In Proceedings of the 28th Conference of the International Group for the Psychology of Mathematics Education, 4:177–184. 

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