Tagoregora. Review on Gora - Rabindranath Tagore

This book has a verry good and effective story. According to the Principle of Replication, the experiment should be repeated 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 then 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 then 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 even be repeated

several times for better results. Conceptually replication does not present any difficulty, but

computationally it does. For example, if an experiment requiring a two-way analysis of variance is

replicated, it will then require a three-way analysis of variance since replication itself may be a

source of variation in the data. 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.

The Principle of Randomizationprovides 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 the extraneous factors(soil

fertility differences in the given case). As such, through the application of the principle of randomization,

we can have a better estimate of the experimental error.

The Principle of Local Controlis another important principle of experimental designs. Under it

the extraneous factor, 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(varieties of rice in our

case), the extraneous factor(soil fertility in our case) 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. Dividing the field into several

homogenous parts is known as ‘blocking’. In general, blocks are the levels at which we hold an

extraneous factor fixed, so that we can measure its contribution to the total 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 factor(s) from the experimental error.