# Non spurious relationship definition of

### Spurious relationship - Wikipedia

Non-spurious definition, not genuine, authentic, or true; not from the claimed, pretended, or proper source; counterfeit. See more. Spurious correlation is often caused by a third factor that is not apparent If skirt lengths are long, that means the stock market is going down;. In statistics, a spurious relationship or spurious correlation is a mathematical relationship in Often one tests a null hypothesis of no correlation between two variables, and chooses in advance to reject the hypothesis if the correlation computed . There are several other relationships defined in statistical analysis as follows.

If the students prepare together in the house, they are bound to get distracted easily and will hamper their preparation.

They will also focus and concentrate less on the subject, which leads to poor grades. On the contrary, when they prepare in a quiet environment, like the library, they tend to concentrate better, and so, they write their paper better. No such connection exists; the size of the hands depend on genes.

### Spurious relationship

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.

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.

## Spurious relationship

Here the spurious correlation in the sample resulted from random selection of a sample that did not reflect the true properties of the underlying population. Because of this, experimentally identified correlations do not represent causal relationships unless spurious relationships can be ruled out.

Experiments[ edit ] In experiments, spurious relationships can often be identified by controlling for other factors, including those that have been theoretically identified as possible confounding factors.

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. 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. On the other hand, if the control culture does not die, then the researcher cannot reject the hypothesis that the drug is efficacious. The heat wave is an example of a hidden or unseen variable, also known as a confounding variable.

Another popular example is a series of Dutch statistics showing a positive correlation between the number of storks nesting in a series of springs and the number of human babies born at that time. Of course there was no causal connection; they were correlated with each other only because they were correlated with the weather nine months before the observations. Because of this, experimentally identified correlations do not represent causal relationships unless spurious relationships can be ruled out.

Experiments In experiments, spurious relationships can often be identified by controlling for other factors, including those that have been theoretically identified as possible confounding factors.

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. 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.

• Spurious Correlation Explained With Examples

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. 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.