This manuscript presents findings from a study about the knowledge for and planned teaching of standard deviation. We investigate how understanding variance as an unbiased (inferential) estimator – not just a descriptive statistic for the variation (spread) in data – is related to teachers’ instruction regarding standard deviation, particularly around the issue of division by n-1. In this regard, the study contributes to our understanding about how knowledge of mathematics beyond the current instructional level, what we refer to as nonlocal mathematics, becomes important for teaching. The findings indicate that acquired knowledge of nonlocal mathematics can play a role in altering teachers’ planned instructional approaches in terms of student activity and cognitive demand in their instruction.
This is an Accepted Manuscript of an article published by Routledge an imprint of Taylor & Francis Group in Research in Mathematics Education on December 2017, available online at: doi: 10.1080/14794802.2017.1333918
Wasserman, Nicholas H.; Casey, Stephanie; Champion, Joe; and Huey, Maryann. (2017). "Statistics as Unbiased Estimators: Exploring the Teaching of Standard Deviation". Research in Mathematics Education, 19(3), 236-256.
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