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Research Methodology - Experiments

Introduction of Experiments

   Posted On :  21.05.2018 01:02 am

The meaning of experiment lies in the process of examining the truth of a statistical hypothesis related to some research problem.

Introduction
 
 
The meaning of experiment lies in the process of examining the truth of a statistical hypothesis related to some research problem. For example, a researcher can conduct an experiment to examine the newly developed medicine. Experiment is of two types: absolute experiment and comparative experiment. When a researcher wants to determine the impact of a fertilizer on the yield of a crop it is a case of absolute experiment. On the other hand, if he wants to determine the impact of one fertilizer as compared to the impact of some other fertilizer, the experiment will then be called as a comparative experiment. Normally, a researcher conducts a comparative experiment when he talks of designs of experiments.

Research design can be of three types:
 
1. Research design in the case of descriptive and diagnostic research studies,
 
2. Research design in the case of exploratory research studies, and
 
3. Research design in the case of hypothesis testing research studies.
 
Here we are mainly concerned with the third one which is Research design in the case of hypothesis testing research studies.
 

Research design in the case of hypothesis testing research studies:

 
 
Hypothesis testing research studies are generally known as experimental studies. This is a study where a researcher tests the hypothesis of causal relationships between variables. This type of study requires some procedures which will not only reduce bias and increase reliability, but will also permit drawing inferences about causality. Most of the times, experiments meet these requirements. Prof. Fisher is considered as the pioneer of this type of studies (experimental studies). He did pioneering work when he was working at Rothamsted Experimental Station in England which was a centre for Agricultural Research. While working there, Prof. Fisher found that by dividing plots into different blocks and then by conducting experiments in each of these blocks whatever information that were collected and inferences drawn from them happened to be more reliable. This was where he was inspired to develop certain experimental designs for testing hypotheses concerning scientific investigations. Nowadays, the experimental design is used in researches relating to almost every discipline of knowledge. Prof. Fisher laid three principles of experimental designs:
 
1. The Principle of Replication
 
2. The Principle of Randomization and
 
3. The Principle of Local Cont
 
 

The Principle Of Replication:

 
 
According to this principle, the experiment should be repeated more than once. Thus, each treatment is applied in many experimental units instead of one. This way the statistical accuracy of the experiments is increased. For example, suppose we are going to examine the effect of two varieties of wheat. Accordingly, we divide the field into two parts and grow one variety in one part and the other variety in the other. Then we compare the yield of the two parts and draw conclusion on that basis. But if we are to apply the principle of replication to this experiment, then we first divide the field into several parts, grow one variety in half of these parts and the other variety in the remaining parts. Then we collect the data of yield of the two varieties and draw conclusion by comparing the same. The result so obtained will be more reliable in comparison to the conclusion we draw without applying the principle of replication. The entire experiment can be repeated several times for better results.
 

The Principle Of Randomization:

 
 
When we conduct an experiment, the principle of randomization provides us a protection against the effects of extraneous factors. This means that this principle indicates that the researcher should design or plan the experiment in such a way that the variations caused by extraneous factors can all be combined under the general heading of ‘chance’. For example, when a researcher grows one variety of wheat , say , in the first half of the parts of a field and the other variety he grows in the other half, then it is just possible that the soil fertility may be different in the first half in comparison to the other half. If this is so the researcher’s result is not realistic. In this situation, he may assign the variety of wheat to be grown in different parts of the field on the basis of some random sampling technique i.e., he may apply randomization principle and protect himself against the effects of the extraneous factors. Therefore, by using the principle of randomization, he can draw a better estimate of the experimental error.
 

The Principle Of Local Control:

 
 
This is another important principle of experimental designs. Under this principle, the extraneous factor which is the known source of variability is made to vary deliberately over as wide a range as necessary. This needs to be done in such a way that the variability it causes can be measured and hence eliminated from the experimental error. The experiment should be planned in such a way that the researcher can perform a two-way analysis of variance, in which the total variability of the data is divided into three components attributed to treatments (varieties of wheat in this case), the extraneous factor (soil fertility in this case) and experimental error. In short, through the principle of local control we can eliminate the variability due to extraneous factors from the experimental error. 
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