Introduction to Experimental Design
See Textbook: Section 15.2
Previously in the course, we have referenced how experiemental design drives our models and not the other way around. Therefore it is essential that statisticians understand how data are collected. Experimental design addresses how the experiment was actually conducted. This typically involves physical layout, logistics, etc., and affects the ANOVA. In our discussions of treatment designs we looked at experimental data in which there were multiple observations made for treatment applications. We referred to these loosely as ‘replicates’. In this lesson, we need to work formally with these multiple observations. This brings us to the right hand side of the schematic diagram;
How many treatments are there?
How many levels of each treatment are there?
If there is more than 1 treatment, how are they related?
Crossed: each level of treatments occur with all levels of other treatments. (Factorial)
Nested: levels of a treatment are unique to different levels of another treatment.
Are treatments fixed or random effects?
Are there continuous covariates? (ANOCOVA)
What is the experimental unit?
An experimental unit is defined as that which recieves a treatment, (e.g., plant, person, plot of ground, petri dish, etc.).
Is there more than one experimental unit? (Split Plot)
How are treatment levels assigned to experimental units?
Completely at random? CRD
Restriction or randomization?
two-dimension: Latin Square
Are there sample units within experimental units? How many true replications are there?
Are there repeated measurements made on experimental units?
The treatment design addresses the natureof the experimental factors under study. The randomization design addresses how the treatments are assigned to experimental units.
- Understand the importance that experimental design has on how we are able to interpret results.
- Be able to articulate how the treatments are being applied within an experiment to the experimental units.
- Understand the critical role that randomization plays within an experiment and why this is important as we interpret results.
- Understanding the role that blocking factors have in restricting randomization within experimental design.