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Extraneous solutions are not too difficult to deal with because they just require checking all solutions for validity. However, more insidious are missing solutions, which can occur when performing operations on expressions that are invalid for certain values of those expressions.
By using one of these methods to account for nuisance variables, researchers can enhance the internal validity of their experiments, ensuring that the effects observed are more likely attributable to the manipulated variables rather than extraneous influences. In the first example provided above, the sex of the patient would be a nuisance variable.
Other variables researchers consider in experimentation are known as the extraneous variables, and are either controllable or confounding (more than one variable at play). Confounding variables are external variables that are not taken into account when conducting an experiment. [6]
Such variables may be designated as either a "controlled variable", "control variable", or "fixed variable". Extraneous variables, if included in a regression analysis as independent variables, may aid a researcher with accurate response parameter estimation, prediction, and goodness of fit, but are not of substantive interest to the hypothesis ...
By controlling for the extraneous variables, the researcher can come closer to understanding the true effect of the independent variable on the dependent variable. In this context the extraneous variables can be controlled for by using multiple regression. The regression uses as independent variables not only the one or ones whose effects on ...
Typically, demand characteristics are considered an extraneous variable, exerting an effect on behavior other than that intended by the experimenter. Pioneering research was conducted on demand characteristics by Martin Orne. [2]
In an economic model, an exogenous variable is one whose measure is determined outside the model and is imposed on the model, and an exogenous change is a change in an exogenous variable. [1]: p. 8 [2]: p. 202 [3]: p. 8 In contrast, an endogenous variable is a variable whose measure is determined by the model. An endogenous change is a change ...
Confounding, in statistics, an extraneous variable in a statistical model that correlates (directly or inversely) with both the dependent variable and the independent variable; Hidden transformation, in computer science, a way to transform a generic constraint satisfaction problem into a binary one by introducing new hidden variables