In any true experiment, a researcher manipulates an independent variable, to influence a dependent variable, or variables.
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A well-designed experiment normally incorporate one or two independent variables, with every other possible factor eliminated, or controlled. There may be more than two dependent variables in any experiment.
For example, a researcher might wish to establish the effect of temperature on the rate of plant growth; temperature is the independent variable. They could regard growth as height, weight, number of fruits produced, or all of these. A whole range of dependent variables arises from one independent variable.
This reduces the risk of 'correlation and causation' errors. Controlled variables are used to reduce the possibility of any other factor influencing changes in the dependent variable, known as confounding variables.
In the above example, the plants must all be given the same amount of water, or this factor could obscure any link between temperature and growth.
The relationship between the independent variable and dependent variable is the basis of most statistical tests, which establish whether there is a real correlation between the two. The results of these tests allow the researcher to accept or reject the null hypothesis, and draw conclusions.