One good reason that we should never conclude causation from...

One good reason that we should never conclude causation from even the strongest correlation or association is that the association between $x$ and $y$ might be due to a third variable $z$ that influences both $x$ and $y$ . If such a variable is not part of the study model, it is called a lurking variable. For each of the following actual associations, identify a possible lurking variable that could be causing it. (a) There is a positive association between the size of a hospital and the death rate among its patients. (b) There is a positive association between the number of seats on a commercial jet and the speed at which the aircraft travels. (c) There is a positive association between shoe size and reading level among elementary school students. (d) There is a positive association between the number of firemen fighting a fire and the amount of damage that the fire causes. (e) Among professional football players there is a negative association between body weight and annual income.

One good reason that we should never conclude causation from even the strongest correlation or association is that the association between $x$ and $y$ might be due to a third variable $z$ that influences both $x$ and $y$ . If such a variable is not part of the study model, it is called a lurking variable. For each of the following actual associations, identify a possible lurking variable that could be causing it.
(a) There is a positive association between the size of a hospital and the death rate among its patients.
(b) There is a positive association between the number of seats on a commercial jet and the speed at which the aircraft travels.
(c) There is a positive association between shoe size and reading level among elementary school students.
(d) There is a positive association between the number of firemen fighting a fire and the amount of damage that the fire causes.
(e) Among professional football players there is a negative association between body weight and annual income.

Labs

Want to see this concept in action?

NEW

Explore this concept interactively to see how it behaves as you change inputs.

View Labs

Key Concepts

Correlation

Correlation is a statistical measure that describes the degree to which two variables move together. It indicates the strength and direction of an association but does not provide evidence that one variable causes changes in another.

Causation

Causation refers to a relationship where one variable directly affects another. Establishing causation requires ruling out other influences and demonstrating that changes in one variable produce changes in the other, often through controlled experiments or rigorous statistical methods.

Lurking Variable

A lurking variable is an unseen factor that may influence both the variables being studied, thereby creating or exaggerating an apparent association between them. It can lead to misleading interpretations by suggesting a relationship where none exists directly between the primary variables.

Confounding Variable

A confounding variable is similar to a lurking variable and affects both the independent variable and the dependent variable, potentially biasing the results of a study. Identifying and controlling for confounding variables is critical to accurately determine the true relationship between the variables of interest.

Transcript

Please give Ace some feedback