The Role of Probability in Developing Learners’ Models of Simulation Approaches to Inference


Authors: 
Hollylynne S. Lee, Helen M. Doerr, Dung Tran, and Jennifer N. Lovett
Year: 
2016
URL: 
http://iase-web.org/documents/SERJ/SERJ15(2)_Lee.pdf
Abstract: 

Repeated sampling approaches to inference that rely on simulations have recently gained prominence in statistics education, and probabilistic concepts are at the core of this approach. In this approach, learners need to develop a mapping among the problem situation, a physical enactment, computer representations, and the underlying randomization and sampling processes. We explicate the role of probability in this approach and draw upon a models and modeling perspective to support the development of teachers’ models for using a repeated sampling approach for inference. We explicate the model development task sequence and examine the teachers’ representations of their conceptualizations of a repeated sampling approach for inference. We propose key conceptualizations that can guide instruction when using simulations and repeated sampling for drawing inferences.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education

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