  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.
