1. Experimental Design All experiments consist of two basic structures:
Design structure
Method of grouping EU’s into homogenous groups or blocks
Treatment structure
Consists of the set of treatments
All experiments consist of two basic structures. The first is the design structure and refers to the method of grouping EU’s into homogenous groups or blocks, that is, the way in which randomization is restricted. The second structure common to all experiments is the treatment structure and consists of the set of treatments (or treatment combinations) selected for evaluation.

2. Choosing the design and treatment structure
Experimental Design
Choosing the design and treatment structure
Design structure - chosen using all available knowledge of the experimental material
Set of treatments - determined by the objectives of the experiment.
Choosing the design structure and treatment structure is very important: The design structure is chosen using all available knowledge of the experimental material, and is chosen independently of the treatment structure. The set of treatments to be evaluated is determined by the objectives of the experiment.

3. Choice of experimental design is very important!
Choosing the appropriate experimental design is very important, because it dictates the appropriate model to be used during the analysis stage of the experiment, and it’s important that the appropriate analysis model be used in order that the method is as sensitive as possible.

4. Experimental Design
Primary objective when choosing a design structure:
Reduction of experimental error
Do this by grouping EU’s so that conditions as uniform as possible.
Grouping must occur BEFORE treatments are assigned!
One of the primary objectives in choosing a design structure is to reduce experimental error. This is accomplished by grouping Experimental Units so that conditions under which treatments are observed are as uniform as possible. It’s important that it be stressed that this grouping occurs prior to assignment of treatments.

5. Experimental Design Types of Design Structures
Completely Randomized (CR)
Randomized Complete Block (RCB)
Latin Square (LS)
If the experimental material is very homogenous, then there need be only one group, and the design is referred to as completely randomized. If more than one group of EU’s is required so that units within a group are more homogenous than units between groups, then the design structure is some type of blocked design. All treatments must appear in each group so that group differences are removed from the total variation.
The three main types of design are: completely randomized, randomized complete block, and latin square

6. Experimental Design Completely Randomised Simplest and most powerful
Treatments assigned randomly
Randomisation not restricted in any way
Completely Randomised: if there are no other sources of variability, this is the most powerful design. It is the simplest design structure possible because treatments are assigned to Experimental Units completely at random. Randomization is therefore not restricted in any way.

7. Experimental Design EXAMPLE 20 cows – same age, weight, breed
Objectives of study: effects of vitamin supply
Treatments: 4 different vitamins (A, B, C, D)
An example where a completely randomised design is appropriate:
An animal scientist has a set of 20 cows of approximately same age, weight and breed. In performing a study of effects of vitamin supply, they randomly assign 5 cows to each of 4 treatment groups (4 different vitamins). The cows form part of an essentially homogenous group, and so there is nothing to block on. In this diagrammatic representation, treatments are labelled A to D, and are randomly assigned to any of the 20 cows, making sure that 5 cows are assigned per treatment. A B C D

8. Experimental Design Randomised Complete Block
EU’s arranged in homogenous groups (= blocks)
Grouping based on a single outside source of variability
One restriction on randomisation
Treatments assigned randomly to EU’s within a block
Every treatment occurs same number of times within each block
Each block contains each treatment
Randomised Complete Block: Experimental Units are arranged in homogenous groups called blocks on the basis of a single outside source of variability, so in other words, there is one restriction on the process of randomization. Treatments are assigned randomly to Experimental Units within a block, and an independent randomization is carried out for each block. Every treatment must occur the same number of times (usually once) within each block, and each block contains each treatment. Therefore, each block is complete with regard to the whole set of treatments. A block is a complete replicate, in other words.

9. Experimental Design EXAMPLE E D C B A
5 different soya bean cultivars (A – E)
3 different planting locations
Source of variation = location = block
An example where a Randomised complete Block design is appropriate:
The seed yield of 5 different soybean cultivars were evaluated in a field test. Three different planting locations were used. A recognized source of variation was the location, and so the experiment was conducted as a randomised complete block with 3 blocks (the locations). Each cultivar was evaluated for seed yield at each location. In this diagrammatic representation, the cultivars are labelled A to E, and each cultivar is planted at each of the three locations (Blocks 1 to 3). E D C B A
Block 1
Block 2
Block 3

10. Experimental Design Latin Square
Blocking occurs in two different ways, so two restrictions on randomisation.
Each row and each column is a complete block
Treatments assigned so each treatment once in each column, and once in each row
Latin Square: this design arises when blocking occurs in two different ways. Now there are two restrictions on randomization. Generally, rows and columns are the terms used when referring to the two blocking factors. Each row and each column are a complete block. Treatments are randomly allocated to EU’s such that each treatment occurs once, and once only in each row, and once and once only, in each column. This means the number of rows, treatments and columns must be equal.

11. Experimental Design EXAMPLE B A C D
Objective: To determine the effects of 4 different diets on liver cholesterol in sheep (A – D)
Sources of variability:
body weight
(groups 1 - 4)
Age
(groups 1 – 4) B A C D 1 2 3 4
An example where a Latin Square design is appropriate: An animal scientist designed an experiment to determine the effects of 4 different diets on liver cholesterol in sheep. They recognized that the sheep they were working with (the EU’s) did not form an entirely homogenous group, and recognised that sources of variability were body weight, and age. There were 4 different weight groups and 4 different age groups. In this diagrammatic representation, the weight groups are labelled A to D, and the age groups are 1 to 4. Treatments were applied as depicted.

12. Experimental Design Treatment Structures Determined by objectives
Imposed (controllable) eg type of protein in diet
Uncontrollable eg gender of animal
Treatment structures:
This refers to the nature of treatments, and how they are arranged. Treatments are experimental conditions applied to, or associated with, Experimental Units. Treatments are compared as measured by their effects on given response variables. When they can be a number of different classes or levels, a treatment is called a factor. The term treatments refers to those factors to compared based on the effects they have on response variables. Treatments can be imposed (controllable) factors such as types of protein in a diet, or uncontrollable factors such as the gender of an animal.

13. Experimental Design Types of Treatments Define either:
Unstructured populations - compare sets of unrelated, qualitative treatments
eg yield of maize hybrids
Remember that treatments are determined by the objectives of the study. Treatments define either structured or unstructured populations.
In experiments that have unstructured treatments, the objective is to compare a set of unrelated, qualitative treatments. For example, an agronomist might conduct a performance trial to compare commercial maize hybrids produced by eight different seed companies. An experiment would be designed to determine if hybrids from the 8 different companies are different, as measured by maize seed yield.

14. Experimental Design Types of Treatments or:
structured populations – compare related populations
May have a group or gradient structure
eg Diet contents or levels of exercise
Treatments can also be structured – these are treatments which define related populations, and may be further classified as having a group or a gradient structure. When they have a group structure, they can be grouped into 2 or more sets of related treatments. Those that have a gradient structure are treatments where there are incremental differences present. An example of a set of treatments with a grouped structure would be a diet that contained different types of maize – for example, normal maize, high-lysine maize etc. An example of a set of gradient treatments would be different levels of exercise. The researcher would then measure the effects of different levels of exercise on weight loss or muscle gain.

15. Experimental Design Types of Treatment Structures Two most common:
One-way treatment classification
Factorial treatment arrangement
The different types of treatments form the various treatment structures.
The two most common or important treatment structures are one-way treatment classification and a factorial treatment arrangement.

16. Experimental Design Types of Treatment Structures
One-way treatment classification
Simplest type
Unstructured or structured
Observations – based on one set
A One-way treatment classification consists of a set of treatments which may be structured (in other words, they have a grouped or gradient composition) or unstructured (in other words, treatments that are unrelated to one another). Observations are classified only on the basis of the one set of treatments or source of variation. It is the simplest type of treatment structure.

17. Experimental Design Types of Treatment Structures
Factorial treatment arrangement
Set of treatment combinations
Qualitative or quantitative
Each factor occurs in combination with every other factor
Provide information about interactions among factors
A factorial treatment arrangement consists of a set of treatment combinations constructed by combining the classes or levels of two or more different factors. Factors may be either qualitative or quantitative in nature. Every class (or level) of one factor occurs in combination with every class (or level) of each other factor in the experiment. So we say that factors are cross-classified. Factorial treatment arrangements provide information about each factor in the experiment, and also information about interactions among the different factors.

18. Planning and pre-experiment protocol
Experimental Design
Planning and pre-experiment protocol
Obtain unbiased estimates of treatment effects
Estimated with adequate precision to detect differences
Max information from resources
Protect against erroneous conclusions
Planning and pre-experiment protocol:
Experiments should be designed to obtain unbiased estimates of treatment effects, treatment differences and experimental error. They also need to be designed and replicated in such a way that treatment effects will be estimated with adequate precision to detect differences, if they truly exist, at the desired probability level. Careful planning and organization before an experiment is initiated can maximize the amount of information gained from the resources utilized. Also, careful planning will help protect against problems associated with violation of basic statistical principles. Although there are relatively few basic principles, they are, for the most part, inflexible, and violation leads to inefficient tests. Such tests may fail to yield valid data and can lead to biologically unsound, or even erroneous, conclusions.

19. Planning and pre-experiment protocol
Experimental Design
Planning and pre-experiment protocol
Preliminary questions:
What is to be accomplished?
What variables will be measured?
What is the population involved?
How many treatments and how arranged?
Controls to be included?
In order to avoid many of the potential pitfalls of experimentation, several questions relating to design and analysis should be addressed in the planning stage of the experiment. Among the more important questions are the following:
What is to be accomplished by the experiment? What are the specific objectives? What variables will be measured? Is the goal to detect significant differences or to estimate numerical quantities? What is the population of inference?
How many treatments are to be included in the experiment? What are they? How are the treatments to be arranged? Is there a one-way classification or a factorial arrangement? How are treatments structured? Is there a grouped structure? Is there a gradient structure, and if so, what levels should be included? Is it necessary to include one or more controls in order to draw meaningful conclusions?

20. Planning and pre-experiment protocol
Experimental Design
Planning and pre-experiment protocol
Preliminary questions cont:
What are the EU’s?
What is the design structure?
Replication?
Variability among EU’s?
Type of data?
Can the desired comparisons be made?
What are the Experimental Units?
How are the treatments to be allocated to the EU’s, in other words, what is the design structure? Are the EU’s relatively homogenous? Are there any identifiable criteria for arranging Experimental Units into more homogenous groups?
How many times does each treatment need to be observed? In other words, how much replication is necessary? What is the smallest difference worth detecting? How much variability exists among Experimental Units? What type of data are to be collected (qualitative or quantitative)? Is the purpose of the analysis descriptive or comparative?
Can the resulting experimental design be analysed? Can the desired comparisons be made? Will the objective be satisfied?