Spurious relationship between variables

Spurious Correlation Explained With Examples

spurious relationship between variables

Spurious is a term used to describe a statistical relationship between two variables that would, at first glance, appear to be causally related, but. A spurious correlation is a relationship wherein two events/variables that actually have no logical connection are inferred to be related due an unseen third. Items 1 - 40 of 43 A spurious relationship implies that although two or more variables are correlated , these variables are not causally related. Correlation analysis.

No such connection exists; the size of the hands depend on genes. The assumption here is that longer the hair, higher the scores. However, the lurking factor here may be that female students got better, may be because they worked harder and more sincerely than the guys.

Spurious relationship - Wikipedia

Or perhaps, they were seniors who already had some experience due to which they fared better. People assume that the more they read, they outgrow their shoes, or their shoes don't fit them as they read better. How wrong, how wrong. The very obvious factor here is age.

As they grow bigger, they tend to develop their reading ability. Along with mental skills, their bodies undergo a change as well, and their feet grow bigger, which is why they outgrow their shoes. Truth be told, it is the fact that eating excessively causes them to be lethargic and lazy, which is why they are not into sports and other activities, which makes them clumsy and obese.

The safety factor here is the security measures for public and private grounds and parks. The connection assumed here is that the more safety measures in place, the fatter the kids get.

Spurious Correlation Explained With Examples

The actual reason is that extra safety and security spoils the fun of sports and games for the kids, which is why they avoid playing at all, and the lack of exercise causes them to gain weight.

This connection turned out to be completely false, when subsequent research discovered that taking HT increased the risk of heart diseases and other disorders.

Back to Top In the Media There have been innumerable instances of spurious correlations in the news. Some of the prominent ones are highlighted here. For example, consider a researcher trying to determine whether a new drug kills bacteria; when the researcher applies the drug to a bacterial culture, the bacteria die.

spurious relationship between variables

But to help in ruling out the presence of a confounding variable, another culture is subjected to conditions that are as nearly identical as possible to those facing the first-mentioned culture, but the second culture is not subjected to the drug.

If there is an unseen confounding factor in those conditions, this control culture will die as well, so that no conclusion of efficacy of the drug can be drawn from the results of the first culture.

Spurious relationship

On the other hand, if the control culture does not die, then the researcher cannot reject the hypothesis that the drug is efficacious. Non-experimental statistical analyses Disciplines whose data are mostly non-experimental, such as economicsusually employ observational data to establish causal relationships.

spurious relationship between variables

The body of statistical techniques used in economics is called econometrics. The main statistical method in econometrics is multivariable regression analysis.

If there is reason to believe that none of the s is caused by y, then estimates of the coefficients are obtained. If the null hypothesis that is rejected, then the alternative hypothesis that and equivalently that causes y cannot be rejected.

spurious relationship between variables

On the other hand, if the null hypothesis that cannot be rejected, then equivalently the hypothesis of no causal effect of on y cannot be rejected.

Here the notion of causality is one of contributory causality: Likewise, a change in is not necessary to change y, because a change in y could be caused by something implicit in the error term or by some other causative explanatory variable included in the model. Regression analysis controls for other relevant variables by including them as regressors explanatory variables. This helps to avoid mistaken inference of causality due to the presence of a third, underlying, variable that influences both the potentially causative variable and the potentially caused variable: In addition, the use of multivariate regression helps to avoid wrongly inferring that an indirect effect of, say x1 e.