Randomized Complete Block Design (RCBD)

The Randomized Complete Block Design may be defined as the design in which the experimental material is divided into blocks/groups of homogeneous experimental units (experimental units have same characteristics) and each block/group contains a complete set of treatments which are assigned at random to the experimental units.

In Randomized Complete Design (CRD), there is no restriction on the allocation of the treatments to experimental units. But in practical life there are situations where there is relatively large variability in the experimental material, it is possible to make blocks (in a simpler sense groups) of the relatively homogeneous experimental material or units. The design applied in such situations is called a Randomized Complete Block Design (RCBD).

Randomized Complete Block Design

RCBD is a one-restriction design, used to control a variable that influences the response variable. The main aim of the restriction is to control the variable causing the variability in response. Efforts of blocking are made to create a situation of homogeneity within the block. Blocking is a source of variability. An example of a blocking factor might be the gender of a patient (by blocking on gender), this is a source of variability controlled for, leading to greater accuracy. RCBD is a mixed model in which one factor is fixed and the other is random. The main assumption of the design is that there is no contact between the treatment and block effect.

Randomized Complete Block design is said to be a complete design because in this design the experimental units and number of treatments are equal. Each treatment occurs in each block.

The general model is defined as

\[Y_{ij}=\mu+\eta_i+\xi_j+e_{ij}\]

where $i=1,2,3\cdots, t$ and $j=1,2,\cdots, b$ with $t$ treatments and $b$ blocks. $\mu$ is the overall mean based on all observations, $\eta_i$ is the effect of the ith treatment response, $\xi$ is the effect of the jth block, and $e_{ij}$ is the corresponding error term which is assumed to be independent and normally distributed with mean zero and constant variance.

The main objective of blocking is to reduce the variability among experimental units within a block as much as possible and to maximize the variation among blocks; the design would not contribute to improving the precision in detecting treatment differences.

Randomized Complete Block Design Experimental Layout

Suppose there are $t$ treatments and $r$ blocks in a randomized complete block design, then each block contains homogeneous plots for one of each treatment. An experimental layout for such a design using four treatments in three blocks is as follows.

Block 1Block 2Block 3
ABC
BCD
CDA
DAB
Randomized Complete Block Design (RCBD)

From the RCBD layout, we can see that

  • The treatments are assigned at random within blocks of adjacent subjects and each of the treatments appears once in a block.
  • The number of blocks represents the number of replications
  • Any treatment can be adjacent to any other treatment, but not to the same treatment within the block.
  • Variation in an experiment is controlled by accounting for spatial effects.

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Completely Randomized Design (CRD)

Introduction to Completely Randomized Design (CRD)

The simplest and non-restricted experimental design, in which the occurrence of each treatment has an equal number of chances, each treatment can be accommodated in the plan, and the replication of each treatment is unequal is known to be a completely randomized design (CRD). In this regard, this design is known as an unrestricted (a design without any condition) design that has one primary factor. In general form, it is also known as a one-way analysis of variance.

Example of CRD

There are three treatments named $A, B$, and $C$ placed randomly in different experimental units.

CAC
BAA
BBC

We can see that from the table above:

  • There may or may not be a repetition of the treatment
  • The only source of variation is the treatment
  • Specific treatment doesn’t need to come in a specific unit.
  • There are three treatments such that each treatment appears three times having P(A)=P(B)=P(C)=3/9.
  • Each treatment appears an equal number of times (it may be unequal i.e. unbalanced)
  • The total number of experimental units is 9.
Completely Randomized Design

Some Advantages of Completely Randomized Design (CRD)

  1. The main advantage of this design is that the analysis of data is simplest even if some unit does not respond due to any reason.
  2. Another advantage of this design is that it provides a maximum degree of freedom for error.
  3. This design is mostly used in laboratory experiments where all the other factors are under the control of the researcher. For example, in a tube experiment, CRD is best because all the factors are under control.

An assumption regarding completely randomized design (CRD) is that the observation in each level of a factor will be independent of each other.

Statistical Model of CRD

The general model with one factor can be defined as

\[Y_{ij}=\mu + \eta_i +e_{ij}\]

where$i=1,2,\cdots,t$ and $j=1,2,\cdots, r_i$ with $t$ treatments and $r$ replication. $\mu$ is the overall mean based on all observations. $eta_i$ is the effect of ith treatment response. $e_{ij}$ is the corresponding error term which is assumed to be independent and normally distributed with mean zero and constant variance for each.

Importance of CRD

  • Simplicity: CRD is the easiest design to implement, in which treatments are assigned randomly to eliminate complex layouts and make them manageable for beginners.
  • Fairness: Randomization ensures each experimental unit has an equal chance of receiving any treatment. The randomization reduces the bias and strengthens the validity of the comparisons between treatments.
  • Flexibility: CRD can accommodate a wide range of experiments with different numbers of treatments and replicates. One can also adjust the design to fit the specific needs.
  • Data Analysis: CRD boasts the simplest form of statistical analysis compared to other designs. This makes it easier to interpret the results and conclude the experiment.
  • Efficiency: CRD allows for utilizing the entire experimental material, maximizing the data collected.

When CRD is a Good Choice

  • Laboratory experiments: Due to the controlled environment, CRD works well for isolating the effects of a single factor in lab settings.
  • Limited treatments: If there are a small number of treatment groups, CRD is a manageable and efficient option.
  • Initial investigations: CRD can be a good starting point for initial explorations of a factor’s effect before moving on to more complex designs.

Summary

The advantages and importance of CRD make it a valuable starting point for many experiments, particularly in controlled laboratory settings. However, it is important to consider limitations like the assumption of homogeneous experimental units, which might not always be realistic in field experiments.

Read from Wikipedia: Completely Randomized Design (CRD)

Design of Experiments Overview (2015)

Objectives of Design of Experiments

Regarding the Design of Experiments: an experiment is usually a test trial or series of tests. The objective of the experiment may either be

  1. Confirmation
  2. Exploration

Designing an experiment means, providing a plan and actual procedure for laying out the experiment. It is a design of any information-gathering exercise where variation is present under the full or no control of the experimenter. The experimenter in the design of experiments is often interested in the effect of some process or intervention (the treatment) on some objects (the experimental units) such as people, parts of people, groups of people, plants, animals, etc. So the experimental design is an efficient procedure for planning experiments so that the data obtained can be analyzed to yield objective conclusions.

In the observational study, the researchers observe individuals and measure variables of interest but do not attempt to influence the response variable, while in an experimental study, the researchers deliberately (purposely) impose some treatment on individuals and then observe the response variables. When the goal is to demonstrate cause and effect, the experiment is the only source of convincing data.

Design of Experiments

Statistical Design

By the Statistical Experimental Design, we refer to the process of planning the experiment, so that the appropriate data will be collected, which may be analyzed by statistical methods resulting in valid and objective conclusions. Thus there are two aspects to any experimental problem:

  1. The design of the experiments
  2. The statistical analysis of the data

Many experimental designs differ from each other primarily in the way, in which the experimental units are classified, before the application of treatment.

Design of Experiments (DOE) helps in

  • Identifying the relationships between cause and effect
  • Provide some understanding of interactions among causative factors
  • Determining the level at which to set the controllable factors to optimize reliability
  • Minimizing the experimental error i.e., noise
  • Improving the robustness of the design or process to variation

Learn more about Design of Experiments Terminology

Basic Principles of Design of Experiments

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