A Review on Comparison of Complete and Incomplete Block Designs

Abstract

The study of strategies for efficient plans for the collection of data, which lead to proper estimates of parameters relevant to the researcher's objective is known as experimental design. Field-based agronomic and genetic research is a decision-based process and many of decisions are required to decide the type of design used to conduct a field experiment, collect, analyze data and interpret the results. Many researchers are locked into particular dimensions that are partly determined by the size or capacity of planting or harvesting equipment, available resources, the size and shape of fields available for research, and colleagues' perceptions, or perhaps even peer pressure. A properly designed experiment for a particular research objective is the basis of all successful experiments. Experimental designs are classified in to complete block and incomplete block designs based on the treatment numbers found in a block. In case of complete block designs, all treatments are found in a block while some treatments found in incomplete block designs. Some of complete block designs are, completely randomized design (CRD), completely randomized block design (RCBD, Latin square (LSD), split plot design (SPD) whereas incomplete block designs are lattice design (LD) and Augmented designs (AD) are most known. Most of the time complete block design is widely used by researchers more than the balanced incomplete block designs because the missing data are computed before analysis. Each research designs had their own level of precision and conditions. Therefore, no single design is best all over designs. Due to this, the present review highlights the basic comparison concepts of complete and incomplete experimental designs and the focus of selection criteria.