Complete Random Design (CRD) – GeeksforGeeks
A completely randomized design (CRD) is one where the treatments are assigned completely at random so that each experimental unit has the same chance of receiving any one treatment.
In CRD, as the name suggests, treatments are assigned completely randomly so that each treatment unit gets the same chance of receiving any one treatment. This is suitable only for the experiments such as laboratory experiments or greenhouse studies etc, where the experiment material is homogeneous and not for heterogeneous studies.
All CRDs with one primary factor are designed by 3 numbers:
- k, indicates number of factors
- L, indicates number of levels
- n, indicates number of replications
Total sample size which indicates number of runs (N=k*L*n)
For example:
- k=1 factor
- L = 4 levels of that single factor (called “1”, “2”, “3” and “4”)
- n = 3 replications per level
- N =L*n = 12 runs
Features of Complete Random Design (CRD):
- The whole field is divided directly into plots (product of replications=treatments).
- In CRD, treatment-wise randomization is done.
- Local Control is not adopted in this case.
- It is divided into 2 component divisions.
- Analysis in CRD is very easy and simple.
Randomization Procedure in Complete Random Design (CRD):
- Each replicate is randomized separately.
- Each treatment has the same probability of being assigned to a given experimental unit within a replicate.
- Each treatment must appear at least once per replicate.
For Example- Given four fertilizer rates applied to ‘Amidon’ wheat and three replicates of each treatment.
where, A = 0 KG N/ha, B = 50 KG N/ha, C = 100 KG N/ha, D = 150 KG N/ha
Fixed vs Random Effect in Complete Random Design (CRD):
- ANOVA assumes the independent variable is fixed in Fixed Effect, while in Random Effect, it assumes an independent variable is random.
- Fixed effects probably produce smaller standard errors, while the Random effect produces larger standard errors.
- The fixed effect has a large number of parameters, whereas the random effect has the small number of parameters.
Advantages of Complete Random Design (CRD):
- It is simple and easy.
- It provides a maximum number of degrees of freedom.
Disadvantages of Complete Random Design (CRD):
- It is less accurate than other designs.
- It reduces precision.
- It increases experimental errors.
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