Basic Principles of Design of Experiments
WHAT IS DESIGN OF EXPERIMENTS?
Design of experiments (DOE) is a systematic method to determine the relationship between factors affecting a process and the output of that process. In other words, it is used to find cause-and-effect relationships. This information is needed to manage process inputs in order to optimize the output.
The basic principles of experimental designs are randomization, replication, and local control. These principles make a valid test of significance possible.
1) Randomization
2) Replication
3) Local Control
(1) RANDOMIZATION:
The first principle of an experimental design is randomization, which is a random process of assigning treatments to the experimental units. The random process implies that every possible allotment of treatments has the same probability. An experimental unit is the smallest division of the experimental material, and treatment means an experimental condition whose effect is to be measured and compared. The purpose of randomization is to remove bias and other sources of extraneous variation which are not controllable. Another advantage of randomization (accompanied by replication) is that it forms the basis of any valid statistical test. Hence, the treatments must be assigned at random to the experimental units. Randomization is usually done by drawing numbered cards from a well-shuffled pack of cards, by drawing numbered balls from a well-shaken container, or by using tables of random numbers.
Randomization is the process of making something random; in various contexts, this involves, for example: generating a random permutation of a sequence (such as when shuffling cards); selecting a random sample of a population (important in statistical sampling).
(2) REPLICATION:
The second principle of an experimental design is replication, which is a repetition of the basic experiment. In other words, it is a complete run for all the treatments to be tested in the experiment. In all experiments, some kind of variation is introduced because of the fact that the experimental units such as individuals or plots of land in agricultural experiments cannot be physically identical. This type of variation can be removed by using a number of experimental units. We, therefore, perform the experiment more than once, i.e., we repeat the basic experiment. An individual repetition is called a replicate. The number, shape, and size of replicates depend upon the nature of the experimental material. Replication is used to:
(i) Secure a more accurate estimate of the experimental error, a term which represents the differences that would be observed if the same treatments were applied several times to the same experimental units;
(ii) Decrease the experimental error and thereby increase precision, which is a measure of the variability of the experimental error.
(3) LOCAL CONTROL:
It has been observed that all extraneous sources of variation are not removed by randomization and replication. This necessitates a refinement of the experimental technique. In other words, we need to choose a design in such a manner that all extraneous sources of variation are brought under control. For this purpose, we make use of local control, a term referring to the amount of balancing, blocking, and grouping of the experimental units. Balancing means that the treatments should be assigned to the experimental units in such a way that the result is a balanced arrangement of the treatments.
Blocking:
Blocking means that experimental units should be collected together to form a relatively homogeneous group. A block is also a replicate. The main purpose of the principle of local control is to increase the efficiency of an experimental design by decreasing the experimental error. The point to remember here is that the term local control should not be confused with the word control. The word control in experimental design is used for a treatment that does not receive any treatment when we need to find out the effectiveness of other treatments through comparison.
Diagram:
ADVANTAGES AND DISADVANTAGES OF DESIGN OF EXPERIMENTS
IMPORTANCE OF DESIGN OF EXPERIMENTS
1. It allows for multiple input factors to be manipulated, determining their effect on the desired output (response).
2. By manipulating multiple inputs at the same time, DOE can identify important interactions that may be missed when experimenting with one factor at a time.
3. All possible combinations can be investigated (full factorial) or only a portion of the possible combinations (fractional factorial).
CONCLUSION
Designed experiments are an advanced and powerful analysis tool during projects. An effective experimenter can filter out the noise and discover significant process factors. The factors can then be used to control response properties in a process and teams can then engineer a process to the exact specification their product or service requires. A well-built experiment can save not only project time but also solve critical problems that have remained unseen in processes. Specifically, interactions of factors can be observed and evaluated. Ultimately, teams will learn what factors matter and what factors do not.
REFERENCES
1. https://www.moresteam.com/toolbox/design-of-experiments.cfm
2. https://www.google.com/search?q=importance+of+design+of+experiments&gs_ivs=1
