Skip to content
Statistics and the student t-test
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
__pycache__
ME3263_lab-00.ipynb
README.md
apt.txt
brittleness_of_glass.pdf
check_lab00.py
environment.yml
pretty_plots.py
student_error-of-mean.pdf

README.md

Lab 0 - Statistics and the Student t-test

https://ugmelab.uconn.edu

Binder Lab 0 notebook

We use statistics to draw conclusions from limited data. No measurement is exact. Every measurement you make has two types of uncertainties, systematic and random. Systematic uncertainties come from faults in your assumptions or equipment. Random uncertainties are associated with unpredictable (or unforeseen at the time) experimental conditions. These can also be due to simplifications of your model. Here are some examples for caliper measurements:

In theory, all uncertainies could be accounted for by factoring in all physics in your readings. In reality, there is a diminishing return on investment for this practice. So we use some statistical insights to draw conclusions.

You can’t perform that action at this time.