Experimental Design Principles

Experimental research designs, which can otherwise be called hypothesis-testing research designs, were originally made by R.A. Fisher in agricultural research in England. Experimental designs are generally used in experimental studies where hypo these are tested. Experimental designs are now used in almost all the areas of scientific studies. Professor Fisher has enumerated three principles of experimental designs:

1. The principle of replication:

The experiment should be reaped more than once. Thus, each treatment is applied in many experimental units instead of one. By doing so, the statistical accuracy of the experiments is increased. For example, suppose we are to examine the effect of two varieties of rice. For this purpose we may divide the field into two parts and grow one variety in one part and the other variety in the other part. We can 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. We can collect the data 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 even be repeated several times for better results. Consequently replication does not present any difficulty, but computationally it does. However, it should be remembered that replication is introduced in order to increase the precision of a study; that is to say, to increase the accuracy with which the main effects and interactions can be estimated.

2. The principle of randomization:

It provides protection, when we conduct an experiment, against the effect of extraneous factors by randomization. In other words, this principle indicates that we 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 instance if we grow one variety of rice say in the first half of the parts of a field and the other variety is grown 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, our results would not be realistic.

In such a situation, we may assign the variety of rice to be grown in different parts of the field on the basis of some random sampling technique i.e., we may apply randomization principle and protect ourselves against the effects of extraneous factors. As such, through the application of the principle of randomization, we can have a better estimate of the experimental error.

3. Principle of local control:

It is another important principle of experimental designs. Under it the extraneous factors, the known source of variability, is made to vary deliberately over as wide a range as necessary and this needs to be done in such a way that the variability it causes can be measured and hence eliminated from the experimental error. This means that we should plan the experiment in a manner that we can perform a two-way analysis of variance, in which the total variability of the data is divided into three components attributed to treatments, the extraneous factor and experimental error. In other words, according to the principle of local control, we first divide the field into several homogeneous parts, known as blocks, and then each such block is divided into parts equal to the number of treatments. Then the treatments are randomly assigned to these parts of a block. In general, blocks are the levels at which we hold an extraneous factors fixed, so that we can measure its contribution to the variability of the data by means of a two-way analysis of variance. In brief, through the principle of local control we can eliminate the variability due to extraneous factors from the experimental error.

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