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,
- OR
- 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 Relationship: General Example, Detecting Spurious Relationships
Famous quotes containing the words spurious and/or relationship:
“To summarize the contentions of this paper then. Firstly, the phrase ‘the meaning of a word’ is a spurious phrase. Secondly and consequently, a re-examination is needed of phrases like the two which I discuss, ‘being a part of the meaning of’ and ‘having the same meaning.’ On these matters, dogmatists require prodding: although history indeed suggests that it may sometimes be better to let sleeping dogmatists lie.”
—J.L. (John Langshaw)
“Sometimes in our relationship to another human being the proper balance of friendship is restored when we put a few grains of impropriety onto our own side of the scale.”
—Friedrich Nietzsche (1844–1900)