Academic Master


Complete Experimental Design

The experiment intentionally imposes the treatment on the group of subjects and objects with the interest of observing the responses. However, it differs from an observation study, which involves the collection and analysis of the data without altering the condition. Its construction and execution directly interfere with the validity of the experiment, so it is important to do experimental design.

However, treatment in the experiment is something that the researcher administers to experiment units. For example, a corn farm is divided into four fields, and each part is treated and applied with different fertilizers to see which one produces the most corn. Another example is a teacher practicing different teaching methodologies on different student groups within the class and investigating which one produces the best result. Therefore, there should be factors that are used in the experiment that control independent variables.

Experimental Design

It is the process of properly organizing an experiment to ensure that it provides the right type of data and enough available data to answer the question of interest effectively. Hence, particular questions that the experiment intends to answer should be identified before experimenting. Also, some of the variability that is expected in the experimental unit should be identified because the primary aim of the experimental design is to decrease the impact of the sources of variability on the answer to the question of interest. Thus, experimental design is used to improve the precision of the answers.


They are constraints developed that are used in the conditions where the experiment should be conducted so that the effecting feedback or answer should be attained. For example, a farmer would like to evaluate new fertilizers. Therefore, he uses the new fertilizer to treat his crops in one field of crops (field A), while on the other field (Field B) he uses the current fertilizer. However, the irrigation system in A is repaired therefore crops receive adequate water, while in B the irrigation system will be repaired in the next season, therefore, the field still have a problem with the supply of water for crops. Finally, the farmer concluded that the new fertilizers were superior.

The issue with the experiment is that the firmer had failed or neglected to control the impact of the difference in the system of irrigation. Hence, it led to experimental bias by favoring certain results over others. To avoid experimental bias, the farmer should have tested his new fertilizer in identical conditions between the two fertilizers (control group); all should receive the same treatment. Therefore, without controlling the outside variables and constraints, he can’t conclude the effect of the new fertilizer and not an irrigation system, which led to better crop production.


It is a technique used in the experimental design where the treatment is assigned at random. Each experimental unit has the same opportunity to receive a specific treatment. However, the technique is used in laboratory experiments where the environmental factors of the experiment are relatively easy to control. However, it is rarely used in fields where environmental factors are difficult to control. Some variations of the randomized experimental designs are randomized block design, replication, and complete randomized design.

For example, a research doctor was investigating the effectiveness and efficiency of four different creams of the skin for the treatment of a specified skin illness. He had eight subjects, and then he divided them into four treatment groups of twenty subjects each. By using a randomized block design, the subjects are assessed, analyzed, and put in blocks of four, depending on the severity of the skin condition. Out of four, the most severe case conditions of the skin were categorized into the first block, then the second block, and then the twentieth block. Four members from each block were randomly sampled, and one member from each of the four treatment groups. The data is collected from these samples for analysis.


Fisher, Ronald. “The Arrangement of Field Experiments.” Journal of the Ministry of Agriculture of Great Britain (1926).

Hunter, W. G., and J. S Hunter. Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley, 2005.

Montgomery, Douglas C. Design, and Analysis of Experiments (8th ed.). Wiley, 2013.



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