Open AccessTHE ROLE OF PROBABILITY IN DEVELOPING LEARNERS’ MODELS OF SIMULATION APPROACHES TO INFERENCE
Author(s) -
Hollylynne S. Lee,
Helen M. Doerr,
Dũng Trần,
Jennifer N. Lovett
Publication year - 2016
Publication title -
statistics education research journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.538
H-Index - 14
ISSN - 1570-1824
DOI - 10.52041/serj.v15i2.249
Subject(s) - inference , sampling (signal processing) , perspective (graphical) , computer science , task (project management) , statistical inference , probabilistic logic , frequentist inference , key (lock) , mathematics education , machine learning , artificial intelligence , psychology , bayesian inference , statistics , mathematics , bayesian probability , management , filter (signal processing) , economics , computer vision , computer security
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.First published November 2016 at Statistics Education Research Journal Archives