Proper experiment design is crucial for technological and scientific advancements, allowing researchers to establish cause-and-effect relationships. Control groups and factorial designs are used to test variables, but applying statistical methods to social and economic studies can be challenging. Selection criteria for focus groups and polls also require expertise in experiment design.
Advances in technology and scientific knowledge are often due to improvements in the use of proper experiment design. Understanding this statistical concept allows researchers to assign cause-and-effect relationships and take the guesswork out of analyzing results. Economic and commercial matters require as much care as scientific research in the design of experiments.
In the scientific design of experiments, the researcher attempts to prove the logical statement: If X, then Y. The converse must also be proved to establish a cause-and-effect relationship: if not X, then not Y. Intuitively, we understand, for example , that a plant needs water to live and if the plant does not get water, it will die. There is therefore a causal relationship between the plant’s needs and water.
The researcher attempts to prove both logical statements through the use of control groups. Ideally, the same research subjects experience the same experimental conditions simultaneously. When this is not possible, as is often the case in biological experiments, a second group of subjects is matched with the first group in as many factors as possible to influence the results. The effectiveness of a diet, for example, can be tested by selecting a control group similar to the test group in age, income, activity level, and number of children. In more critical experiments, the design of experiments will include the actual matching of single subjects; that is, subject number 1A will be the same age, gender, activity level, and starting weight as subject number 1B, but will receive the test diet, whereas subject 1A will not.
Factorial designs allow you to study more than one variable within the same experiment, but with the same rigor as control groups, by applying the mathematics of probability. Mendel’s discoveries in genetics were due to factorial experiments and observations. In these experiments, two or more independent factors are tested at two or more levels. For example, subjects can be subdivided into three independent variables: regular diet, diet A, or diet B. Each of these subgroups is subdivided again, based on the duration of the diet, three weeks or six weeks.
Statistical methods are fairly easy to apply to the design of experiments in the natural science realms. In the social sciences, which include behavioral studies, they are more difficult. In studying economics and commerce, the subjects are people and companies. These subjects do not lend themselves easily to study.
Marketing studies often depend on focus groups, whose selection care is essential. Knowledge of proper experiment design is necessary to establish selection criteria. Polls, a common tool of product managers and policy groups, also require this expertise in their design.
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