Causation vs Correlation: Differences, Designs and Examples

Correlation vs Causation: Definition

Before clarifying causation vs correlation, let’s define them first. Both these terms mean there is a certain proximity between some variables which are being analyzed. What is different then? In fact, the first term states mere facts about variables appearing together in some circumstances. And another one explains actual links between them. Now let’s proceed to detailed definitions and review them.

What Is Causation: Definition

Causation is a term which directly implies that one variable causes another. Apart from their similarity, it takes into account what actual dependencies they have in between. 

Finding how exactly one variable depends on another is important for statistical data analysis received from focus groups. This is called causal research. When doing it, you need to understand first which parameter of your group is a cause and which is an effect. For example, you measure the popularity of some e-commerce stores among your target audience of online shoppers. One store’s popularity is decreasing while it keeps updating its website often. Most probably, users don’t like such frequent changes.

Correlation Research: Definition

Correlation research aims to indicate the association level between two variables. Correlation can be:

1. positive, when one variable is likely to grow or decrease together with another.
2. negative, when one variable’s development typically occurs when another one declines and vice versa.

Finding out how variables correlate with each other requires collecting data about them, observing trends in it and analyzing what changes are there. 

If you need some correlation research examples, here’s one. Suppose you notice that people from your target group are more likely to visit local malls when average temperature outside decreases. There is no evidence it’s actually causing them to go shopping more often but this correlation can be measured if necessary.

If you don’t have time to collect data, consider turning to professionals having proven experience in running research studies. Say ‘write my dissertation’ or ‘do my research paper’ to get comprehensive help.

Correlation vs Causation: What Is the Difference

As we’ve already shown above, the main difference between correlation and causation is whether assumptions are made about the relationship between variables, or not. In the latter case you aim to conclude whether A causes B or vice versa. But when you study correlations, your goal is just to trace the level of similarity between A’s and B’s trends.

Keep in mind that correlation doesn’t imply causation, but causation always implies correlation, since linked variables have to correlate visibly. Thus, tracing the pattern of them correlating is often the first step to finding how they are related.

Correlation Is Not Causation: Why

If we look closer into this causation vs correlation case, we can find two official reasons why a certain similarity can be clearly labeled as ‘correlation, not causation’. They are:

1. Third variable problem When you cannot explain a connection between two variables unless you take a third variable into account. If this third one is actually influencing both others, leaving it out of scope during your research design leads to making wrong assumptions or getting confusing results.
2. Directionality problem When you cannot prove the cause-and-effect relationship between these two variables because you don’t have enough data to find out which one is actually influencing which. This often happens during experimental studies when confounding factors are examined.

Causation and Correlation: Examples

Let’s take a look at some correlation vs causation examples in order to illustrate the information about statistical relationships provided above.

Suppose that some researchers are analyzing health data in some country. They have found a strong positive correlation between respondents' level of exercise and numbers of skin cancer cases. Despite this correlation being reliable and backed up by results from multiple populations, it is decided that additional research is needed before assuming that exercise is causing cancer.

Let’s also suppose that another factor has been found: exposure to sunlight. Additional explorations have shown that people who live in locations where weather is more often clear are going outside much more often. Therefore inhabitants of such regions have more exercise than the inhabitants of locations less associated with sunny weather. And there are bigger risks of skin cancer for them as well. In this case a third factor is influencing both variables.

Correlation vs Causation: Takeaways

We’ve made a detailed review of the correlation and causation problem. In this article we compare both these concepts and discuss their differences. Their treatment in different occasions has also been examined closely. This information should help you in future research and with composing quality papers.