Smart experimental design
Designing experiments well is the key to obtaining reliable and impactful research results. Poorly planned experiments can waste time and resources and lead to misleading conclusions. In this training, you will discover the essential principles behind experimental design and understand why clear, structured planning makes experiments more efficient.
You will put these concepts into practice by working on realistic biotechnology research scenarios and using tools in R to design or optimize experiments. Dedicated time will be provided for discussing your own experimental design challenges.
Learning outcomes
- Understand the core principles of experimental design and why they influence research validity and efficiency
- Define clear research questions and identify key factors, sources of variability, and strategies to manage them
- Apply essential design concepts such as randomization, blocking, replication, and power to real-world scenarios
- Compare and implement different experimental design strategies, including regression methods, factorial and fractional designs, using R tools
- Evaluate and improve experimental plans through power studies and collaborative case-based exercises
Approach
The training blends core principles with hands-on exercises in R, complemented by group discussions and case-based applications to real-world biotech research.
Required skills
You should have experience with R, and we offer a free online preparatory course that you can follow.
More information can be found on: Basic Statistics in R. If this course is full, contact janick.mathys@vib.be to obtain a seat.
Extra information
This course is part of the learning path Statistics