So what is this causation correlation confusion?
When it rains, you get wet. When it does not rain, you do not get wet. Every time it rains, you get wet. Of course, while you are under the skies. This is causation. You can see the logic that if every time X happens, then Y has to happen. If any time X did not happen, we never see Y.
Correlation, on the other hand, is the fact that Y and X get together. It does not necessarily mean one affects the other. Rather, a third factor, such as Z influences them both. For example, you might get wet, and you might see the rainbow. These two correlate but they do not cause each other. The rain could cause both to happen, yet rain falls and then cease after some time, likewise the rainbow, it’s clearly seen in the sky after it rains.
It is important to distinguish between the causation relationship and the correlation relationship. You have to be very specific when you use this terminology because its meaning is very different.
For example, as people get jobs their happiness is raised. A research may reach this conclusion after surveying people about their work status, and their happiness level. A relationship can be measured using correlation analysis. Now, the correlation analysis may show a significant association between these two variables. Yet, we cannot claim that because people have jobs, they are happy. Even though, it makes sense that when people have a job, their happiness increases. The causation conclusion would require much work to prove, yet correlation can be sufficient to confirm your point, which says there is association between having a job and one’s happiness.