Experimental design is a key part of agricultural engineering experiments. Well-designed experiments allow researchers to obtain maximum information to meet their objectives. Key steps in planning an experiment include recognizing the problem, selecting factors and response variables, choosing an experimental design, conducting the experiment, performing statistical analysis, and drawing conclusions. Proper experimental design principles like replication, randomization, and blocking help ensure simplicity, efficiency, and validity of results.
2. • An experiment is a test or a series of tests
• Experiments are used widely in the engineering
world
– Process characterization & optimization
– Evaluation of material properties
– Product design & development
– Component & system tolerance determination
• “All experiments are designed experiments, some
are poorly designed, some are well-designed”
3. Experimental Design
• Is the creation of a detailed experimental plan
that allows you to obtain the maximum amount
of information specific to your objectives
(Science and the Global Environment,2017)
4. Most Common Purpose of Conducting Experiment
• To explore new technologies, new crops, and new areas of
production
• To develop a basic understanding of the factors that control
production
• To develop new technologies that are superior to existing
technologies
• To study the effect of changes in the factors of production and
to identify optimal levels
• To demonstrate new knowledge to growers and get feedback
from end – users about the acceptability of new technologies
5. Steps for Planning, Conducting and Analyzing an Experiment
• The practical steps needed for planning and conducting an experiment
include: recognizing the goal of the experiment, choice of factors, choice of
response, choice of the design, analysis and then drawing conclusions. This
pretty much covers the steps involved in the scientific method.
• Recognition of & statement of problem
• Choice of factors, levels, and ranges
• Selection of the response variable(s)/dependent
• Choice of design
• Conducting the experiment
• Statistical analysis
• Drawing conclusions, and making recommendations
6. Experimental Design/Designed Experiment
- It is a plan for the assignment of the treatments to the plots in the
experiments
- Treatments are imposed by investigator using standard protocols
- Designs differ primarily in the way the plots are grouped before
• the treatments are applied
• May infer that the response was due the treatments
Designing a Research Experiments
• Designing an experiment simply means planning an experiment so that
information will be collected is relevant to the problem under investigation.
• too often data collected are of little or no value in an attempted solution to
the problem because little or no prior consideration was given to the design
of the experiment.
• design of experiment -> complete sequence of the steps taken ahead of time
to ensure that the appropriate data will be obtained in a way that permits an
objective analysis leading to valid with respect to the stated problem.
7. WELL – PLANNED
EXPERIMENT
• Simplicity
• Degree of precision
• Absence of systematic error
• Range of validity of conclusion
• Calculation of degree of uncertainty
8. Two Key Considerations in Designing An Experiment;
1. Simplicity – choose the simplest experimental design among many
possible candidates to achieve the same proposed objective(s).
2. Efficiency – >efficient as possible, that is, every effort should be made
to save time, money, personal and experimental materials.
- simple design is also efficient both statistically and economically.
Three Basic Principles in Achieving Optimal level of Simplicity and
Efficiency;
1. Replication – repetition of treatments in an experiment.
2. Randomization – this process involves random allocation of treatments to
the experimental units.
-law of chance applicable to our experimental data and ensures that the data
are free from any systematical error.
3. Blocking (Local Control) –>grouping of the experimental units ->within
the group are more homogenous than are units in different groups.
"All experiments are designed experiments, it is just that some are poorly
designed and a some are well-designed."
9. Engineering Experiments
• Reduce time to design/develop
new products & processes
• Improve performance of
existing processes
• Improve reliability and
performance of products
• Achieve product & process
robustness
• Evaluation of materials, design
alternatives, setting component
& system tolerances, etc.
Some of the objectives
10. Four Eras in the History of DOX
• The agricultural origins, 1918 – 1940s
– R. A. Fisher & his co-workers
– Profound impact on agricultural science
– Factorial designs, ANOVA
• The first industrial era, 1951 – late 1970s
– Box & Wilson, response surfaces
– Applications in the chemical & process industries
• The second industrial era, late 1970s – 1990
– Quality improvement initiatives in many companies
– Taguchi and robust parameter design, process robustness
• The modern era, beginning circa 1990
11. Strategy of Experimentation
• “Best-guess” experiments
• One-factor-at-a-time (OFAT) experiments
– Sometimes associated with the “scientific” or
“engineering” method
– 0Devastated by interaction, also very inefficient
• Statistically designed experiments
– Based on Fisher’s factorial concept
12. Planning, Conducting &
Analyzing an Experiment
1. Recognition of & statement of problem
2. Choice of factors, levels, and ranges
3. Selection of the response variable(s)
4. Choice of design
5. Conducting the experiment
6. Statistical analysis
7. Drawing conclusions, recommendations
13. Planning, Conducting &
Analyzing an Experiment
• Get statistical thinking involved early
• Your non-statistical knowledge is crucial to
success
• Pre-experimental planning
• Think and experiment sequentially
