Assuming that because one event follows another, the first event caused the second.

Believing that a relationship between two variables is always purely coincidental.

Assuming that two variables that move together must have a common cause.

Mistaking a correlation for a negative causal relationship.

Causality always implies correlation, but correlation never implies causality.

Correlation and causality are interchangeable terms in statistical analysis.

If the correlation between two variables is highly statistically significant, this suggests a causal relationship.

Causality means that two variables move together, while correlation implies that changes in one variable lead to changes in the other.

Correlation means that two variables move together, while causality implies that changes in one variable lead to changes in the other.

Smoking causes lung cancer, as confirmed by extensive medical research.

A country’s economic growth causes improved healthcare and improved healthcare causes a higher life expectancy.

Ice cream sales and drowning incidents both increase because of higher summer temperatures.

People who exercise regularly tend to have better cardiovascular health.

A variable that is influenced by both variables in the correlation.

A variable that is only correlated with one of the two variables in question.

A variable that independently causes both variables to be correlated.

A misleading statistical artifact caused by chance.

It is a pure coincidence.

Food stamps cause poverty, i.e. receiving food stamps decreases your motivation to provide for yourself and thus make you poor.

Observing correlation between poverty and food stamps does not tell us if there is any causality (and if so, in which direction). The correct answer can be any of the above or below.

Poverty causes food stamps, i.e. being poor has a causal impact on receiving food stamps.

The causality goes both ways. Being poor has a causal impact on receiving food stamps, but food stamps also decrease your motivation and thus make you poor.