Summary
Randomization is one of the three core principles of design of experiments and is essential for obtaining an unbiased estimate of experimental error.
It ensures that every treatment has an equal chance of being assigned to any experimental unit, thereby reducing personal bias.
Randomization enables the valid use of probability theory and advanced statistical techniques in data analysis.
It can be carried out using methods such as the chit method, random number tables, or statistical software like R.
In the given RCBD example, R is used to randomly allocate 15 treatments across 3 blocks.
There are three principles of designs of experiments:
Replication
Randomization
Local Control
In a previous blog, we have seen how replication provides an estimate of an experimental error.
For a sound experiment, we need to estimate experimental error and that estimate must be an unbiased estimate.
Randomization helps us to get an unbiased estimate of experiment error.
Definition
When all the treatments have equal chances (probability) of being allocated to all the experimental units it is called randomization.
Experimental units can be seeds, plants, animals etc. depending on your research.
The procedure of randomization varies with the experimental design used.
Randomization also allows us to use probability theory and different statistical techniques.
The complex statistical techniques can be used only if randomization is used.
Randomization also helps us to tackle personal bias towards a any treatment.
Different methods for randomization
- Chit method
- Random number table
- By using R software
Chit Method
Chit method is used when number of treatments are low. It uses chits.
The number of chits are equal to number of treatments. On each chit, one treatment is mentioned exclusively.
Chits are randomly selected and treatments.
Random number table
Random number tables are specialized table used for selecting random values. In order to see random table click here
By using R software
Case: Suppose there are 15 treatments and in a Randomized Complete Block Design there are 3 blocks. Carry out randomization for the above mentioned case.
R Code Used
# Generate treatment vector of 15 treatments
a <- paste("T", c(1:15), sep = "")
# Draw random sample (repeat three times)
sample(a, 15, replace = FALSE)
Output from R
Randomization 1:
"T13" "T8" "T2" "T7" "T11" "T5" "T10" "T9" "T14"
"T12" "T6" "T15" "T3" "T4" "T1"
Randomization 2:
"T1" "T8" "T10" "T3" "T12" "T9" "T13" "T7"
"T15" "T5" "T6" "T14" "T2" "T4" "T11"
Randomization 3:
"T2" "T9" "T15" "T5" "T11" "T1" "T12" "T8"
"T14" "T13" "T4" "T10" "T7" "T3" "T6"
How arrangement will look like
| Plot no |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
| Block 1 |
T13 |
T8 |
T2 |
T7 |
T11 |
T5 |
T10 |
T9 |
T14 |
T12 |
T6 |
T15 |
T3 |
T4 |
T1 |
| Block 2 |
T1 |
T8 |
T10 |
T3 |
T12 |
T9 |
T13 |
T7 |
T15 |
T5 |
T6 |
T14 |
T2 |
T4 |
T11 |
| Block 3 |
T2 |
T9 |
T15 |
T5 |
T11 |
T1 |
T12 |
T8 |
T14 |
T13 |
T4 |
T10 |
T7 |
T3 |
T6 |
References
Gupta, S. C., and V. K. Kapoor. Fundamentals of applied statistics. Sulthan Chand & Sons, 2019
Rangaswamy, R (2010). A textbook of Agricultural Statistics (2nd Edn). New Age International Publishers, New Delhi.