Why association does not imply causation

Which one we believe may very well depend on what we expect or want to believe. This is nicely illustrated in the excellent xkcd web comic:. Meanwhile, please send us your instructive examples to help people tell the difference between correlation and causation. Booklet containing 15 examples submitted by Royal Colleges where Evidence-Based Medicine has benefited clinical practice.

Navigate this website. Association is not the same as causation. I can haz cheese girders? Click the cartoon to read the full article. Can measurements show if a treatment works? An article discussing errors to avoid when testing treatments. GET-IT Jargon Buster Select a term treatment or recommendation is accepted by the people who are affected by it, or who are implementing it, in a study or in practice. A statistical association between two variables merely implies that knowing the value of one variable provides information about the value of the other.

It does not necessarily imply that one causes the other. Suppose that we want to know if acute trauma to a joint an exposure causes chronic knee osteoarthritis an outcome. Because it would be unethical to conduct an experiment whereby we deliberately inflict joint trauma to assess its effects on chronic knee osteoarthritis, we decide to tackle this question by using observational data from a hospital registry.

To claim that this association represents a causal effect, we need to first rule out two possible issues that lead to a non-causal association:.

Only if we can rule out these biases can we begin to think that association may imply causation. Confounding occurs when an exposure and an outcome share a common cause the confounder; Figure 1. Failure to control for the confounder makes it appear that there is an association between the exposure and the outcome, whereas in fact both are caused by the confounder and are not related to each other at all or as strongly. Figure 1. Causal diagram illustrating the structure of confounding.

In our example , it is plausible that joint trauma and knee osteoarthritis share a common cause — high impact sport the confounder. That is, individuals involved in high impact sport may be more susceptible to both acute joint trauma and chronic knee osteoarthritis through repeated use. Of course, circumstances can be that straightforward occasionally, but assuming that they are is never a good idea because you will often jump to the wrong conclusions.

Just because correlation is evident, that doesn't mean that A causes B. In statistics, it's a logical fallacy to suggest that correlation proves causation, and no one will take you seriously if your research falls into this trap. There are many variables must be examined when looking the relationship between two events.

You will usually find other factors that have an impact, and it might be these factors that are responsible for the correlation. It seems pretty self-explanatory, but it's not always easy to understand exactly what this phrase means until you examine it carefully. First of all, it is important to understand what a correlation is and what a causation is. A correlation is a mutual relationship or a connection between two variables.

Causation is the relationship between cause and effect. So, when a cause results in an effect, that's a causation. In other words, correlation between two events or variables simply indicates that a relationship exists, whereas causation is more specific and says that one event actually causes the other.

When we say that correlation does not imply cause, we mean that just because you can see a connection or a mutual relationship between two variables, it doesn't necessarily mean that one causes the other. Of course, it might be the case that one event or variable causes the other, but we can't know that by looking at the correlation alone. More research would be necessary before that conclusion could be reached. Why is the relationship between these two things important?

To put it simply, we often need to know if one event or variable causes another when we carry out research. For example, if we want to find out if a drug is having a positive effect on a patient, we need to understand the causality. If a patient gets better after taking a certain drug, was it the drug that caused the improvement? Or was something else happening? This is just one example of how cause and effect relationships can be important when research is being carried out, but there are many other examples that illustrate the same point.

Any situation in which an outcome from a process has to be analyzed will deal with cause and effect relationships. This isn't just important in medicine or science. It can be used in social research, political science, and other areas. For example, in a controlled experiment we can try to carefully match two groups, and randomly apply a treatment or intervention to only one of the groups.

The principle of randomization is key in experimental design, and understanding this context can change what we are able to infer from statistical tests. At the end of that time, we also gather skin cancer rates for this large group. We will end up with a dataset which has been experimentally designed to test the relationship between exercise and skin cancer! Because exercise was directly manipulated in the experiment via random assignment, it will not be systematically related to any other variables that could be different between these two groups assuming all other aspects of the study are valid.

This means that in this case, because our data was derived via sound experimental design, a positive correlation between exercise and skin cancer would be meaningful evidence for causality. Correlation vs. Correlation tests for a relationship between two variables. However, seeing two variables moving together does not necessarily mean we know whether one variable causes the other to occur.

There may be a third, lurking variable that that makes the relationship appear stronger or weaker than it actually is.