Causation indicates that one event causes another. Correlation only identifies that there is a relationship between two events or outcomes.
If you were to collect data on the sale of ice cream cones and swimming pools throughout the year, you would likely find a strong positive correlation between the two as sales of both increase during the summer months. If you make the mistake of assuming correlation implies causation, you would incorrectly claim that an increase in ice cream cone sales causes people to buy swimming pools. However, this isn’t the case since you can attribute the increase in both to another variable—likely the warmer weather people experience during the summer. So although a correlation is present, you can’t support causation.
In another correlation versus causation example, it may not be as easy to identify whether causation is present with two variables. For example, you could find a correlation between the amount someone exercises and their reported levels of happiness. While it’s possible an increase in exercise is causing an increase in happiness, you can’t say for sure that it’s the cause since there could be another unknown variable that has a more significant influence on a person’s mood.
The homeless chart per state is of the number of people in shelters.
Correlation could indicate the poor states have less homeless.
Causation could indicate the reason they have less homeless in shelters is because the have no shelters.
If you look at many of these poor states, you may find less shelter and services exist for the homeless, homeless is more punished by law, or other factors making it less likely for someone to stay there or to be counted as homeless there.
This is why many say you can make a chart show anything you want it to, and you need to be critical when looking at people’s data.