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In statistics, a spurious relationship (or, sometimes, spurious correlation) is a mathematical relationship in which two occurrences have no logical connection, yet it may be implied that they do, due to a certain third, unseen factor (referred to as a "confounding factor" or "lurking variable"). The spurious relationship gives an impression of a worthy link between two groups that is invalid when objectively examined. Statistics is a type of data analysis which practice includes the planning, summarizing, and interpreting of observations of a system possibly followed by predicting or forecasting of future events based on a mathematical model of the system being observed. ...
In science, a mathematical relationship describes how one quantity is related to another. ...
General example
An example of a spurious relationship can be illuminated examining a city's ice cream sales. These sales are highest when the city's rate of drownings is highest. To allege that ice cream sales cause drowning would be to imply a spurious relationship between the two. In reality, a heat wave may have caused both. The heat wave is an example of a hidden or unseen variable. Missing image Ice cream is often served on a stick Boxes of ice cream are often found in stores in a display freezer. ...
A heat wave is a prolonged period of excessively hot weather, which may be accompanied by excessive humidity. ...
Statistics The term is commonly used in statistics and in particular in experimental research techniques. Experimental research attempts to understand and predict causal relationships (X → Y). A non-causal correlation can be spuriously created by an antecedent which causes both (W → X & Y). Intervening variables (X → W → Y), if undetected, may make indirect causation look direct. Because of this, experimentally identified correlations do not represent causal relationships unless spurious relationships can be ruled out. Statistics is a type of data analysis which practice includes the planning, summarizing, and interpreting of observations of a system possibly followed by predicting or forecasting of future events based on a mathematical model of the system being observed. ...
Experimental research designs are used for the controlled testing of causal processes. ...
In probability theory and statistics, correlation, also called correlation coefficient, is a numeric measure of the strength of linear relationship between two random variables. ...
This article is about causality as it is used in many different fields. ...
In practice, three conditions must be met in order to conclude that X causes Y, directly or indirectly:
- X must precede Y
- Y must not occur when X does not occur
- Y must occur whenever X occurs
Spurious relationships can often be identified by considering whether any of these three conditions have been violated.
The final condition may be relaxed in the case of indirect causation. For example, consider a pistol duel. Two men face off and fire at each other. If one man dies as a result of the other man's shot, we can rightly conclude that the other man caused his death. However, if a doctor saves the wounded man's life (thus violating the third premise), this does not undermine causation, only direct causation. The biological damage (W) sustained from the shot (X) causes death (Y), not the shot itself, allowing medical intervention. A duel or duel of honour is a formalised type of armed combat in which two individuals participate. ...
See a more detailed discussion at causation. This article is about causality as it is used in many different fields. ...
See also Correlation implies causation, also known as cum hoc ergo propter hoc (Latin for with this, therefore because of this) and false cause, is a logical fallacy by which two events that occur together are claimed to be cause and effect. ...
Joint effect is a logical fallacy of causation in which two phenomena that have a common cause are thought to be cause and effect themselves. ...
External links and references - Burns, William C., "Spurious Correlations", 1997.
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