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Graphical model: Whereas a mediator is a factor in the causal chain (top), a confounder is a spurious factor incorrectly implying causation (bottom). In statistics, a spurious relationship or spurious correlation [1] [2] is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third ...
Spurious may refer to: Spurious relationship in statistics; Spurious emission or spurious tone in radio engineering; Spurious key in cryptography; Spurious interrupt ...
In radio communication, a spurious emission is any component of a radiated radio frequency signal the complete suppression of which would not impair the integrity of the modulation type or the information being transmitted. [1] A radiated signal outside of a transmitter's assigned channel is an example of a spurious emission. [2]
Spurious relationship – Apparent, but false, correlation between causally-independent variables; Synchronicity – Jungian concept of the meaningfulness of acausal coincidences; Teleology – Thinking in terms of destiny or purpose
False precision (also called overprecision, fake precision, misplaced precision, and spurious precision) occurs when numerical data are presented in a manner that implies better precision than is justified; since precision is a limit to accuracy (in the ISO definition of accuracy), this often leads to overconfidence in the accuracy, named precision bias.
The phenomenon of spurious correlation of ratios is one of the main motives for the field of compositional data analysis, which deals with the analysis of variables that carry only relative information, such as proportions, percentages and parts-per-million. [3] [4] Spurious correlation is distinct from misconceptions about correlation and ...
In mathematics, an extraneous solution (or spurious solution) is one which emerges from the process of solving a problem but is not a valid solution to it. [1] A missing solution is a valid one which is lost during the solution process.
The first to introduce and analyse the concept of spurious—or nonsense—regression was Udny Yule in 1926. [2] Before the 1980s, many economists used linear regressions on non-stationary time series data, which Nobel laureate Clive Granger and Paul Newbold showed to be a dangerous approach that could produce spurious correlation, [3] since standard detrending techniques can result in data ...