Spurious correlation refers to the correlation between two variables that do not have any causal relationship between them. It is a statistical term used to describe a situation where two variables appear to be related to each other, but actually, they are not. In other words, spurious correlation occurs when a correlation between two variables is due to chance or a third variable that is influencing both variables.

For example, let's take the case of ice cream sales and crime rates. It has been observed that ice cream sales and crime rates tend to increase during the summer months. One might be tempted to conclude that higher ice cream sales cause an increase in crime rates. However, this correlation is spurious, as the real cause is the summer heat, which causes people to buy more ice cream and also leads to increased aggression and violence.

Some common causes of spurious correlation include:

- Third variables: A third variable that is influencing both variables can create a spurious correlation. For example, a study found a correlation between the number of storks and the birth rate in a certain area. However, this correlation was spurious, as the real cause was that both variables were influenced by the rural nature of the area.

- Sampling bias: A skewed sample can create a spurious correlation. For example, a study found a correlation between the number of cigarettes smoked and the risk of lung cancer. However, this correlation was spurious, as the sample only included smokers, who are already at a higher risk of lung cancer.

- Measurement errors: Inaccurate measurements can create a spurious correlation. For example, a study found a correlation between shoe size and reading ability in children. However, this correlation was spurious, as the shoe size was measured incorrectly.

In conclusion, spurious correlation is a statistical term used to describe a situation where a correlation between two variables is due to chance or a third variable that is influencing both variables. It is important to identify spurious correlations to avoid making incorrect conclusions and decisions based on faulty data.