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Advanced Experimental Designs

Advanced Experimental Designs

Learn about advanced experimental designs to account for constraints: time, resources, material heterogeneity, randomisation restrictions as well as repeated measures. The construction of avanced designs and their analysis is covered.

Course Details

Learning Objectives

Learning Objectives:

Upon completion of this module, participants will be able to:

  • Make the difference between advanced experimental designs, such as randomised block designs, Latin square designs, designs with covariates, split-splot designs and repeated measures designs
  • Choose an experimental design based on study objectives and experimental constraints
  • Construct and implement the chosen experimental design
  • Perform the data analysis based on the study design and underlying assumptions
  • Interpret experimental results with more confidence and know the scope of the conclusions

Target Audience

Target Audience:

This module is aimed at all the scientific staff who need to design experiments based on constraints whether physical or budgetary and who make decisions based on the data collected.



This module deals with the construction of advanced designs used to account for experimental constraints.

  • An applied knowledge of the fundamental principles in the design of experiments including the concepts involved in Analysis of Variance (ANOVA) is required, whether by having followed the module Introduction to the Design of Experiments DOE or by having a similar level.
  • Moreover, participants should master the essential tools in statistics - descriptive statistics, both numerical (mean, standard deviation, standard error, and so on) and graphical (histogram, box-plot, scatter plot, etc.), hypothesis testing and confidence intervals - either by having attended the training module Fundamental Tools in Statistics for Research or by possessing a similar background.

Course Outline

Course Outline:
  • Brief Review of Statistical Inference and the Logic behind Statistical Testing
  • Confidence Intervals, Standard Errors and P-Values
  • Accounting for Variability in Experimental Units
  • Review of Factorial Designs
  • Advanced Designs
    • Randomized Complete & Incomplete Block Designs
    • Balanced Incomplete Block Designs
    • Latin Square Designs
    • Designs with Covariates
    • Repeated Measures Designs
    • Split-Plot Designs
  • ANOVA for Advanced Designs
  • Using Real Data, Interpretation of the Results

Practical Info

Practical Info:


Recommended Duration: 2 days

Course Materials:

  • Course notes on statistical techniques
  • Sample datasets