A lurking variable (confounding factor or variable, or simply a confound or confounder) is a "hidden" variable in a statistical or research model that affects the variables in question but is not known or acknowledged, and thus (potentially) distorts the resulting data. This hidden third variable causes the two measured variables to falsely appear to be in a causal relation. Such a relation between two observed variables is termed a spurious relationship. An experiment that fails to take a confounding variable into account is said to have poor internal validity. It has been suggested that Causalism be merged into this article or section. ... ... Internal validity is a term pertaining to scientific research that signifies the extent to which the conditions within a research design were conducive to drawing the conclusions the researcher was interested in drawing. ...
For example, ice cream consumption and murder rates are highly correlated. Now, does ice cream incite murder or does murder increase the demand for ice cream? Neither: they are joint effects of a common cause or lurking variable, namely, hot weather. Another look at the sample shows that it failed to account for the time of year, including the fact that both rates rise in the summertime.
In statistical experimental design, attempts are made to remove lurking variables from the experiment. Because we can never be certain that observational data are not hiding a lurking variable that influences both x and y, it is never safe to conclude that a linear model demonstrates a causal relationship with 100% certainty, no matter how strong the linear association. The first statistician to consider a methodology for the design of experiments was Sir Ronald A. Fisher. ...
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