<Table> <Tr> <Td> </Td> <Td> This article includes a list of references, but its sources remain unclear because it has insufficient inline citations . Please help to improve this article by introducing more precise citations. (April 2013) (Learn how and when to remove this template message) </Td> </Tr> </Table> <Tr> <Td> </Td> <Td> This article includes a list of references, but its sources remain unclear because it has insufficient inline citations . Please help to improve this article by introducing more precise citations. (April 2013) (Learn how and when to remove this template message) </Td> </Tr> <P> In statistics, a spurious relationship or spurious correlation is a mathematical relationship in which two or more events or variables are not causally related to each other, yet it may be wrongly inferred that they are, due to either coincidence or the presence of a certain third, unseen factor (referred to as a "common response variable", "confounding factor", or "lurking variable"). </P> <P> A well - known case of a spurious relationship can be found in the time - series literature, where a spurious regression is a regression that provides misleading statistical evidence of a linear relationship between independent non-stationary variables . In fact, the non-stationarity may be due to the presence of a unit root in both variables . In particular, any two nominal economic variables are likely to be correlated with each other, even when neither has a causal effect on the other, because each equals a real variable times the price level, and the common presence of the price level in the two data series imparts correlation to them . (See also Spurious correlation of ratios .) </P>

When would we say that the relationship between two variables x and y is spurious