Statistics deals with the study of gathering, observing, calculating, and interpreting numerical data. It is full of experiments and research. A statistical experiment is defined as an ordered procedure which is performed with the objective of verifying, and determining the validity of the hypothesis. Before performing any experiment, some specific questions for which the experiment is intended should be clearly identified. To minimise the variability effect on the result of interest, the experiment has to be designed. So, the researcher will design the experiments for the purpose of improvement of precision. It is called experimental design or the design of experiments(DOE). In this article, let us discuss the definition and example of experimental design in detail.

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## Experimental Design Definition

In Statistics, the experimental design or the design of experiment (DOE) is defined as the design of an information-gathering experiment in which a variation is present or not, and it should be performed under the full control of the researcher. This term is generally used for controlled experiments. These experiments minimise the effects of the variable to increase the reliability of the results. In this design, the process of an experimental unit may include a group of people, plants, animals, etc.

## Types of Experimental Designs

There are different types of experimental designs of research. They are:

- Pre-experimental Research Design
- True-experimental Research Design
- Quasi-Experimental Research Design

In this article, we are going to discuss these different experimental designs for research with examples.

### Pre-experimental Research Design

The simplest form of experimental research design in Statistics is the pre-experimental research design. In this method, a group or various groups are kept under observation, after some factors are recognised for the cause and effect. This method is usually conducted in order to understand whether further investigations are needed for the targeted group. That is why this process is considered to be cost-effective. This method is classified into three types, namely,

- Static Group Comparison
- One-group Pretest-posttest Experimental Research Design
- One-shot Case Study Experimental Research Design

### True-experimental Research Design

This is the most accurate form of experimental research design as it relies on the statistical hypothesis to prove or disprove the hypothesis. This is the most commonly used method implemented in Physical Science. True experimental research design is the only method that establishes the cause and effect relationship within the groups. The factors which need to be satisfied in this method are:

- Random variable
- Variable can be manipulated by the researcher
- Control Groups (A group of participants are familiar with the experimental group, but the experimental rules do not apply to them)
- Experimental Group (Research participants where experimental rules are applied)

### Quasi-Experimental Design

A quasi-experimental design is similar to a true experimental design, but there is a difference between the two.

In a true experiment design, the participants of the group are randomly assigned. So, every unit has an equal chance of getting into the experimental group.

In a quasi-experimental design, the participants of the groups are not randomly assigned. So, the researcher cannot make a cause or effect conclusion. Thus, it is not possible to assign the participants to the group.

Apart from these types of experimental design research in statistics, there are other two methods used in the research process such as randomized block design and completely randomized design.

### Randomised Block Design

The randomised block design is preferred in the case when the researcher is clear about the distinct difference among the group of objects. In this design, the experimental units are classified into subgroups of similar categories. Those groups are randomly assigned to the group of treatment. The blocks are classified in such a way in the variability within each block should be less than the variability among the blocks. This block design is quite efficient as it reduces the variability and produces a better estimation.

**Example:**

In a drug testing experiment, the researcher believes that age is the most significant factor. So he divides the units according to the age groups such as

- Under 15 years old
- 15 – 35 years old
- 36 – 55 years old
- Over 55 years old

### Completely Randomised Design

Of all the types, the simplest type of experimental design is the completely randomized design, in which the participants are randomly assigned to the treatment groups. The main advantage of using this method is that it avoids bias and controls the role of chance. This method provides a solid foundation for Statistical analysis as it allows the use of probability theory.

## Application of Experimental Design

The concept of experimental design is applied to Engineering, Natural Science and Social Science as well. The areas in which the experimental designs used are:

- Evaluation of physical structures, materials and components
- Chemical formulations
- Computer programs
- Opinion polls
- Natural experiments
- Statistical surveys

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