Learn about advanced experimental designs to account for experimental various types of constraints such as time, available resources, material heterogeneity, randomization restrictions when certain factors are more difficult or costly to change than others, different sizes of experimental units as well as repeated measures.
In this course, the construction of advanced designs and their statistical analysis is covered with the help of real case studies.
Course Outline
Session 1: Heterogeneity Constraint – Designs Taking into Account Several Constraints
- Randomised Complete Block Designs
- Latin Squares
- Analysis of Covariance
- Statistical Analysis of the Designs
Session 2: Constraints to Randomisation – Physical or Time Constraints
- Split-Plot Designs
- Repeated Measures
- Statistical Analysis of the Designs
Course Duration
The recommended course duration is 2 online sessions.
Target Audience
This course is aimed at engineers, researchers, biologists and depending on the target audience, the course is adapted. On the one hand by using examples specific to the field of application and on the other hand by presenting specific tools or applications, if applicable.
This module deals with the construction of advanced experimental designs to take into account experimental constraints.
- Participants must know the essential tools of descriptive statistics and inferential statistics – mean, standard deviation, standard error, median, graphical tools such as histograms, box plots, hypothesis tests, confidence intervals, etc. – i.e. having followed the Fundamental Statistical Tools module or have an equivalent level.
- An applied knowledge of the basic principles of experimental design is required. In addition, the analysis of data from these experimental designs is based on the analysis of variance technique: knowledge of ANOVA is necessary, i.e. having followed the Introduction to the Design of Experiments DoE module or have an equivalent level.