Variables

The term "variable" has different meanings in different disicipline. We say they are "discipline-dependent." In math it often denotes a unknown quantity. In computer science it is something else. In statistics, Wikipedia says,

variables refer to measurable attributes, as these typically vary over time or between individuals. Variables can be discrete (taking values from a finite or countable set), continuous (having a continuous distribution function), or neither. Temperature is a continuous variable, while the number of legs of an animal is a discrete variable. This concept of a variable is widely used in the natural, medical and social sciences.

In causal models, a distinction is made between "independent variables" and "dependent variables," the latter being expected to vary in value in response to changes in the former. In other words, an independent variable is presumed to potentially affect a dependent one. In experiments, independent variables include factors that can be altered or chosen by the researcher independent of other factors. For example, in an experiment to test whether or not the boiling point of water changes with altitude, the altitude is under direct control and is the independent variable, and the boiling point is presumed to depend upon it and is therefore the dependent variable. The collection of results from an experiment, or information to be used to draw conclusions, is known as data. It is often important to consider which variables to allow for, or to directly control or eliminate, in the design of experiments.

There are also quasi-independent variables, which are those variables that are used by researcher as a grouping mechanism, without manipulating the variable. An example of this would be separating people into groups by their gender. Gender cannot be manipulated, but it is used as a way to group. Another example would be separating people on the amount of coffee they drank before beginning an experiment. The researcher cannot change the past, but can use it to differentiate the groups.

While independent variables can refer to quantities and qualities that are under experimental control, they can also include extraneous factors that influence results in a confusing or undesired manner.

In general, if strongly confounding variables exist that can substantially affect the result, then this makes it more difficult to interpret the results. For example, a study into the incidence of cancer with age will also have to take into account variables such as income (poorer people may have less healthy lives), location (some cancers vary depending on diet and sunlight), stress and lifestyle issues (cancer may be related to these more than age), and so on. Failure to at least consider these factors can lead to grossly inaccurate deductions. For this reason, controlling unwanted variables is important in research.

See also: extraneous variables, intervening variable, and level of measurement

Entrepreneurs need to know about variables to analyze data they need for marketing, sales, finance, etc.

There has been a ton of stuff written on variables. At the very least, read the Wikipedia article and follow the links.

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