Cancelled: The return of reproducible research - proper statistical methods (2025)

Welcome to The return of reproducible research: proper statistical methods (2025)

Description: There is a long history of the application of statistical methods on collected data (for example data from experiments). The current replication crisis in science is evidence that sometimes (maybe even often) statistical methods are used wrongly or their results misinterpreted. In this course we give a coherent introduction to statistical analyses and their interpretation that can help avoid these problems.     

In this course we introduce a statistical mindset ? a way of thinking: a statistical paradigm of evidence interpretation. We will focus on conditional probabilities: is the question the patient is asking ?what is the probability of having a positive test result given I am sick?? or is it ?what is the probability of being sick given a positive test result??. We answer this question ? and discuss why the two questions are indeed very different questions.    We proceed to analysing data with probabilistic modelling. We do that by first discussing data-generating processes and causal models, including using these to identify the correct variables to control for in the statistical analysis. Then we proceed to linear regression (a simple linear regression and a linear regression with both continuous and qualitative explanatory variables including potentially interaction effects). We potentially also consider other types of models (for example logistic regression and/or correlated measurements).    

We will use R (https://www.r-project.org) and the Stan software for Bayesian inference (https://mc-stan.org).

Prerequisites: Working knowledge of R. (For example as obtained through the PhD course ?Data Science Using R?.)

Learning objectives: Analysing data using statistical methods ensuring reproducible research findings, including  probabilistic modelling and using software for Bayesian inference.

Teaching methods: Oral presentations, exercises, hand-in.

Criteria for assessment: The students need to participate actively and they will get a written hand-in assignment that must be approved to pass the course.

Key literature: The course does not follow any particular text book, but rather the course slides and supplied notes will be the main course material.

Organizer: Mikkel Meyer Andersen

Lecturers: Mikkel Meyer Andersen

ECTS: 2.0

Time: 24 April and 1 May 2025

Place: Aalborg University, Fibigerstræde 11 room 39

Zip code: 9220

City: Aalborg

Maximal number of participants: 40

Deadline: 03 April 2025

Important information concerning PhD courses: 

There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.

For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.

To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at  phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.