However, correlation does not equal causation. For years tobacco companies tried to cast doubt on the link between smoking and lung cancer, often using “correlation is not causation!” type propaganda. Rather, in cases of correlation, one thing or event predicts another. The problem arises when people attribute causation to correlation. Weight gain in pregnancy and pre-eclampsia (Thing B causes Thing A): This is an interesting case of reversed causation that I blogged about a few years ago. Let's walk through an example … In these cases, extra vetting is needed before a correlation can qualify as causation. These are extreme examples. Example: If it rained every time you planned a picnic one summer, this does not prove that planning a picnic causes rain or that rain causes picnics. This is why we commonly say “correlation does not imply causation.” A strong correlation might indicate causality, but there could easily be other explanations: It may be the result of random chance, where the variables appear to be related, but there is no true underlying relationship. You’ve heard it a million times: correlation doesn’t mean causation. The phrase "correlation does not imply causation" refers to the inability to legitimately deduce a cause-and-effect relationship between two events or variables solely on the basis of an observed association or correlation between them. Therefore, we cannot make statements about how coffee causes any effect on health. Correlation alone does not prove causation A basic and often-repeated principle in science stating that a correlation must be investigated before it can be concluded that one thing causes another. What is an example of correlation but not causation? While scientists may shun the results from these studies as unreliable, the data you gather may still give you useful insight (think trends). Even though two things maintain a relationship, it doesn’t mean there is a cause and effect relationship between them. A correlation is simply a recognized relationship between two things or events, but it does not imply causation. Participants in this sample were not randomly assigned to drink coffee and there was no control group. Although, correlation does not necessarily imply causation, and these examples show the dangers of not understanding the difference between correlation and causation in the real world. So, proving correlation vs causation – or in this example, UX causing confusion – isn’t as straightforward as when using a random experimental study. Simply put, correlation does not equal causation. Well, here’s a humorous look at this topic that I think drives home the point. Although correlations are very useful for psychological research, misconceptions can quickly form from them. Causation. Looking for examples of correlation and causation? They may have evidence from real- world experiences that indicate a correlation between the two variables, but correlation does not imply causation! When an article says that causation was found, this means that the researchers found that changes in one variable they measured directly caused changes in the other. Still need help? Often times, people naively state a change in one variable causes a change in another variable. Can correlation ever equal causation?