Spurious Relationship

In statistics, a spurious relationship (or, sometimes, spurious correlation or spurious regression) is a mathematical relationship in which two events or variables have no direct causal connection, yet it may be wrongly inferred that they do, due to either coincidence or the presence of a certain third, unseen factor (referred to as a "confounding factor" or "lurking variable"). Suppose there is found to be a correlation between A and B. Aside from coincidence, there are three possible relationships:

A causes B,
B causes A,
C causes both A and B.

In the last case there is a spurious correlation between A and B. In a regression model where A is regressed on B but C is actually the true causal factor for A, this misleading choice of independent variable (B instead of C) is called specification error.

Because correlation can arise from the presence of a lurking variable rather than from direct causation, it is often said that "Correlation does not imply causation".

Read more about Spurious RelationshipGeneral Example, Detecting Spurious Relationships

Other articles related to "spurious relationship, relationships, spurious, relationship":

Detecting Spurious Relationships - Non-experimental Statistical Analyses
... as economics usually employ pre-existing data rather than experimental data to establish causal relationships and to determine that they are not spurious ... Typically a linear relationship such as is postulated, in which is the dependent variable (hypothesized to be the caused variable), for j=1...k is the jth ... in the regression, so that effect will not be picked up as a spurious effect of the potentially causative variable of interest ...

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