The Common Misconceptions Surrounding Correlation and Causation

It is important to understand the difference between correlation and causation, as this can have a big impact on how you understand research and decision-making. For example, if you are trying to determine the cause of a problem, you need to make sure that you are actually looking at the cause and not just a correlation.

Many people will report two problems that are escalating at the same time and believe that one causes the other. Or as one problem increases, another one decreases. There could be a connection between the two, or one could cause the other. Or the connection can be spurious, which means that there is no relationship and the two things just happen to occur at the same time. This is often what conspiracy theorists don’t understand (or want to).

There are many factors to consider when determining whether or not there is a correlation or causation between two things. Below are some key points to keep in mind.

Defining Correlation and Causation

Correlation is the association between two variables. It does not imply that one variable causes the other; it simply means that they are related. For example, there is a correlation between ice cream sales and murder rates; this does not mean that eating ice cream causes people to commit murder.

Causation, on the other hand, is the relationship between a cause and its effect. It means that one variable is responsible for the change in the other variable. For example, smoking causes cancer; this means that smoking is the cause of cancer, and not just a correlation.

It is important to understand the difference between correlation and causation, as they are often confused in everyday language.

How to Differentiate Between Correlation and Causation in Research

There are a few things you can do to help differentiate between correlation and causation in research:

1. Look for a cause-and-effect relationship. In order to be certain that you are dealing with causation, there must be a clear cause-and-effect relationship between the two variables. One variable must precede the other. If we want to say that smoking causes lung cancer, smoking must come before someone gets lung cancer. If someone gets lung cancer before he/she starts to smoke, there is no causal relationship. 

2. Control for other variables. In order to rule out the possibility that another variable is causing the change in the first variable, you must control for it in your research. In other words, there might be other factors that could contribute to smoking causing cancer. Sometimes researchers will use statistical regression to help account for other factors, but ultimately experimental research will isolate the variables as much as possible to decrease research error. By the way, all research has error, and researchers understand that. That’s why researchers are continually refining their research studies to triangulate their results.

3. Experiments typically identify causation because researchers will manipulate one variable to see if there’s a change in the dependent variable. I would hardly want to believe an ethical researcher would conduct a research study where one group would be asked to smoke and another group would not to see if the smoking group had cancer. 

However, research can study changes in the lungs over time in people who smoke versus people who do not. Although this isn’t a true experiment, it does a better job of explaining causation.

Different Types of Research Methods That Can Help Identify Correlations and Causes

There are a few different types of research methods that can help identify correlations and causes. One is called observational studies. This involves observing people or things in their natural environment without changing anything about the situation. Another common method is surveys, which involve asking people questions about their opinions or experiences.

Both of these methods can be helpful in identifying correlations, but they can't always tell us whether a correlation is actually caused by something. For that, we need to look at experiments.

In an experiment, researchers will change one thing (the independent variable) and see how that affects another thing (the dependent variable). By doing this, they can start to understand the cause-and-effect relationship between two things.

Exploring Cases of False Correlations

Let's explore some examples of false correlations to help you better understand the difference between correlation and causation. Just because two events happen at the same time, or one event happens after another, doesn't necessarily mean that one caused the other.

For example, let's say you live in a city where it snows a lot in the winter. You might notice that there's a correlation between the amount of snowfall and the number of people who get sick. But that doesn't necessarily mean that the snow is causing people to get sick. It could just be a coincidence. There could also be a lot of other factors that make these related, such as people huddling more indoors in dry heat conditions.

Tips to Avoid Mistaking Correlation as Causation

Now that we've explored the difference between correlation and causation, let's look at some tips to avoid mistaking the two.

First, be sure to consider all of the factors that could be influencing the results of the study. Just because two things are correlated doesn't mean that one caused the other. There could be another variable–or several–that causes both of them. Good researchers will consider these other factors and write about them in their discussion sections.

Second, look for other studies that replicate the results. If you find a correlation in this study, see if another researcher found it in another geographic area or among a different population. This will help to confirm that the results aren't just a fluke.

Third, if you intend to share these results on social media or elsewhere, point out whether it’s correlation or causation. Don’t be excited that this particular research study verified what you believe (or hope to believe).

Conclusion

At the end of the day, correlation and causation are two different things, and it's important to be able to differentiate between the two. Correlation does not equal causation, and just because two things happen together doesn't mean that one thing caused the other.

It's important to understand which research methods determine causation, and to be aware of the potential for confounding variables. Survey research can be a useful tool for exploring correlation, but it's not always the best tool for determining causation.

Ultimately, it's up to researchers to use the right methods and analyze the data correctly to determine if there is a causal relationship between two variables.