Randomized block design (RBD) is a form of experimental design used in agricultural and biological research. It is a type of blocking design which assigns treatments to blocks of experimental units in a random fashion. RBD is a powerful tool for reducing the effects of extraneous variables and for increasing the precision of statistical tests. This article reviews the use of RBD in agricultural and biological experiments, describes its advantages and limitations, and provides guidance for selecting and setting up an RBD.
RBD is used to reduce the effects of extraneous variables in experiments. It does this by randomly assigning treatments to blocks of experimental units and by controlling for potential sources of variation. This helps to reduce variation due to environmental factors, such as soil type or climatic conditions. RBD is also used to increase the precision of statistical tests. It does this by increasing the number of replicates and by increasing the power of the tests.
The advantages of RBD include its ease of implementation and the ability to quickly compare treatments. It also provides a consistent and reliable way to reduce the effects of extraneous variables. Furthermore, its use of randomization and blocking increases the precision of statistical tests.
The main limitation of RBD is its reliance on randomization. While randomization can reduce the effects of extraneous variables, it can also introduce additional sources of variation. This can lead to inaccurate results and invalid conclusions. Careful consideration must be given to the design of the experiment in order to ensure that the effects of randomization are minimized.
In conclusion, randomized block design is a powerful tool for reducing the effects of extraneous variables and for increasing the precision of statistical tests. It is a useful technique for agricultural and biological experiments, but care must be taken to ensure that the effects of randomization are minimized.
References
Bates, D., & Watts, D. G. (1988). Nonlinear regression analysis and its applications. New York: Wiley.
Harvey, A. (2005). Design and analysis of experiments (2nd ed.). Hoboken, NJ: Wiley.
Kirk, R. E. (1995). Experimental design: Procedures for the behavioral sciences (3rd ed.). Thousand Oaks, CA: Sage Publications.
Mathers, N. (2009). Comparison of experimental designs for yield trials. Crop Science, 49(3), 895-903. https://doi.org/10.2135/cropsci2008.05.0288
Steel, R. G. D., & Torrie, J. H. (1960). Principles and procedures of statistics. New York: McGraw-Hill.