Question

Why can't correlations tell us anything about cause and effect?  Going further, what kind of an experimental design would you need to explain cause and effect?  Provide an example to illustrate your point.

Answer 1

Why can't correlations tell us anything about cause and effect?  

Two or more variables considered to be related, in a statistical context, if their values change so that as the value of one variable increases or decreases so does the value of the other variable (although it may be in the opposite direction).

For example, for the two variables "hours worked" and "income earned" there is a relationship between the two if the increase in hours worked is associated with an increase in income earned. If we consider the two variables "price" and "purchasing power", as the price of goods increases a person's ability to buy these goods decreases (assuming a constant income).


Correlation is a statistical measure (expressed as a number) that describes the size and direction of a relationship between two or more variables. A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable.


Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events. This is also referred to as cause and effect.


Theoretically, the difference between the two types of relationships are easy to identify — an action or occurrence can cause another (e.g. smoking causes an increase in the risk of developing lung cancer), or it can correlate with another (e.g. smoking is correlated with alcoholism, but it does not cause alcoholism). In practice, however, it remains difficult to clearly establish cause and effect, compared with establishing correlation.

what kind of an experimental design would you need to explain cause and effect? Provide an example to illustrate your point.

Both cross-sectional and longitudinal research studies are observational. They are both conducted without any interference to the study participants. Cross-sectional research is conducted at a single point in time while a longitudinal study can be conducted over many years.

For example, let’s say researchers wanted to find out if older adults who gardened had lower blood pressure than older adults who did not garden. In a cross-sectional study, the researchers might select 100 people from different backgrounds, ask them about their gardening habits and measure their blood pressure. The study would be conducted at approximately the same period of time (say, over a week). In a longitudinal study, the questions and measurements would be the same. But the researchers would follow the participants over time. They may record the answers and measurements every year.

One major advantage of longitudinal research is that over time, researchers are more able to provide a cause-and-effect relationship. With the blood pressure example above, cross-sectional research wouldn’t give researchers information about what blood pressure readings were before the study. For example, participants may have had lower blood pressure before gardening. Longitudinal research can detect changes over time, both at the group and at the individual level.