Comparing Different Uncertainty Measures to Quantify Measurement Uncertainties in High School Science Experiments


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Authors

DOI:

https://doi.org/10.51724/ijpce.v14i1.214

Keywords:

variance;, science education, measures of spread, simulation

Abstract

Interpreting experimental data in high school experiments can be a difficult task for students, especially when there is large variation in the data. At the same time, calculating the standard deviation poses a challenge for students. In this article, we look at alternative uncertainty measures to describe the variation in data sets. A comparison is done in terms of mathematical complexity and statistical quality. The determination of mathematical complexity is based on different mathematics curricula. The statistical quality is determined using a Monte Carlo simulation in which these uncertainty measures are compared to the standard deviation. Results indicate that an increase in complexity goes hand in hand with quality. Additionally, we propose a sequence of these uncertainty measures with increasing mathematical complexity and increasing quality. As such, this work provides a theoretical background to implement uncertainty measures suitable for different educational levels.

Author Biography

Burkhard Priemer, Humboldt-Universität zu Berlin

Department head of the Physics Education Department of the Humboldt University of Berlin.

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Published

08/19/2022

How to Cite

Kok, K., & Priemer, B. (2022). Comparing Different Uncertainty Measures to Quantify Measurement Uncertainties in High School Science Experiments. International Journal of Physics and Chemistry Education, 14(1), 1–9. https://doi.org/10.51724/ijpce.v14i1.214