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Information for thesis students
  • Introduction
  • Pre-thesis
  • During the project
  • Office, Labs & Infrastructure
  • Grading & Plagiarism
  • Thesis & Defense
  • Post-thesis
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  • Thesis
  • Tips & Tricks
  • Abstract
  • Statistics
  • Defense

Thesis & Defense

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Last updated 1 year ago

Thesis

The thesis commonly consists of a document with an abstract, introduction, background, methods, results, discussion, and conclusion. You can send me (parts of) your thesis for feedback before handing it in. Remember that you need to do this early enough otherwise I might not have time.

Tips & Tricks

Here are some tips&tricks for your thesis. This section is continously extended. Let me know if you have recommendations on what to add here.

Abstract

Here is a nice piece of information on how to write an abstract.

Statistics

First, a disclaimer: Statistics is a research field in itself and I am by no means an expert. Also, in a thesis there is often not enough time to collect sufficient amounts of data to draw 'final' conclusions from a statistical analysis. Thus, I might recommend in some cases to not do any statistics at all. In any case, spend some time (and potentially some meetings with me) thinking about the usability of statistics.

Here are some resources to get you started:

Defense

The defense starts with an introduction by one of the supervisors. Afterwards you are expected to give a ~25-minute presentation which is followed by questions from the external examiner and the supervisors. The question-session will take no longer than 90 minutes but in many cases its shorter. After the defense you will need to leave the room so that we can discuss your grade. Finally, you receive your grade and hopefully congratulations!

I recorded a short introduction to my workflow here . I mostly collect (and potentially pre-process) data in MATLAB. That data I import to R for data visualization and statistics. In the video I used mixed linear models for the data analysis.

If you want to know more about statistics I can highly recommend Daniel Lakens. Here is a book of Daniel:

We often use p-values but they are also often criticized. And here is a short video on the interpretation of p-values and here is a shiny app about the distribution of p-values .

https://youtu.be/eCVUbobgoxs?si=rTqmuyNt-HHEDfj1
https://lakens.github.io/statistical_inferences/
https://youtu.be/RVxHlsIw_Do?si=78SC7hyMtwctSTdz
https://shiny.ieis.tue.nl/d_p_power/
From
https://xkcd.com/1403/