Statistics as unbiased estimators: Exploring the teaching of standard deviation
Research in Mathematics Education
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.
Link to Published Version
Wasserman, N. H., Casey, S., Champion, J., & Huey, M. (2017). Statistics as unbiased estimators: Exploring the teaching of standard deviation. Research in Mathematics Education, 19(3), 236–256. https://doi.org/10.1080/14794802.2017.1333918