Students learn about uncertainty in repeated measurements through simple Colab programming.
This homework assignment introduces students to ideas about uncertainty from repeated measurements. The tutorial leverages worked-examples to introduce students to how to calculate the mean, standard deviation, and standard uncertainty in the mean and explores how these and the associated distributions change as more or less data are collected. Students respond to questions in Markdown and run (and edit) Python Code Cells to view distributions and calculate quantities.
Students should be able to:
Relate the quantities of means, standard deviations, and standard uncertainty in the mean to characteristics of a distribution of repeated measurements.
Describe why/how standard deviation is used to represent the uncertainty in a single measurement from a set of repeated trials.
Describe why/how standard uncertainty in the mean is used to represent the uncertainty in the mean of a set of repeated trials.
Describe whether and how these quantities change when more data are collected.
Analyze data computationally including Python.
Experimentation Goals
These experimentation goals are most strongly represented in the explicit learning goals described above:
Student Decision Making
In this lab, students have the opportunity to:
Discovery
Determine results previously known to:
Duration
30 minutes
Equipment Required
Implementation Tips
This works as a brief homework assignment or a pre-cursor to a lab activity. It is written in the context of a pendulum activity.
How This Fits in Your Course
This is a great first homework for a course to get students thinking big picture about sources of uncertainty through repeated measurements and getting students started with quantifying that uncertainty.
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Natasha Holmes
Lauren Bauman
Adrian Madsen
CC BY-NC-SA
Attribution, Non-Commercial, Share Alike. Others can share and adapt for non-commercial purposes, must attribute and share with the same license.
