spurious correlation


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Noun1.spurious correlation - a correlation between two variables (e.g., between the number of electric motors in the home and grades at school) that does not result from any direct relation between them (buying electric motors will not raise grades) but from their relation to other variables
statistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters
correlational statistics, correlation - a statistical relation between two or more variables such that systematic changes in the value of one variable are accompanied by systematic changes in the other
Based on WordNet 3.0, Farlex clipart collection. © 2003-2012 Princeton University, Farlex Inc.
References in periodicals archive ?
The researchers carry out several tests for a spurious correlation between the passage of CAP laws and homicide rates.
But now we hear planted voices all day that this solution has weakened the centre, and thus weakened the country a spurious correlation that has persisted for seven decades.
Common experiences as roommates could create a spurious correlation between volunteering as a solicitor for the university by one roommate and giving by another.
However, whether in time domain or in frequency domain, the spurious correlation brought by the common interference imposes a big problem on unmasking the true cooperation between the weak neural sources.
I can't use any moisturizer, any makeup, or all pills." (People generate long litanies of sensitivities, piling one spurious correlation on another.)
The inability to solve the spurious correlation problem within a statistical framework
"The article asserts a spurious correlation, in my view, between veteran unemployment and a rise in the number of veterans who file for VA (Department of Veterans Affairs) disability compensation," Gillums says.
Marmol (1998) suggested that spurious correlation generally occurs in regressions including fractionally integrated processes.
Additionally, if manufacturing firms cluster among a few states with deteriorating fiscal conditions, a spurious correlation may arise.
The exploratory equation also uses almost all the independent variables in one and two lag periods because of the "a priori assumption that the growth rate of capital stock is stationary" over the long run, and because of "the time lag between investment decision and investment expenditure." The author uses several equations and reduces the number of parameters to obtain t-values that are at least free of spurious correlation, but suffer from multicollinearity, because of the many interrelated variables used (ten in the original formulation).
Their main mistake has been to fall into what statisticians call the 'spurious correlation trap'.