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Basics of Research Process

# Causation vs Correlation: Differences, Designs and Examples

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Anyone who’s been doing any kind of research must have come across the correlation vs causation juxtaposition. These two dependency types are common when you analyze statistical variables received by processing data about some target group. Their differences are highly important to remember when doing research. Which is why we’ll discuss them today.

Despite it is always said that ‘correlation does not imply causation’, this doesn’t mean one of these parameters is more informative or more important than another. Most probably you’ll need to calculate both of them so that your topic gets properly evaluated. So let’s go and check them in detail with our dissertation help service!

## 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.

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## Causation vs Correlation: Frequently Asked Questions

### 1. What is the third variable problem?

Third variable problem deals with an unaccounted factor which actually influences statistical values in question. Such factors are extraneous variables other than your variables of interest and thus could affect your results. It is crucial that you don’t omit influencing factors before assuming dependencies among your results.

### 2. What are the spurious correlations?

Spurious correlations occur when two variables appear to be related through hidden third variables. The problem with such similarities is that it is difficult to analyze them without unearthing an actual factor that influences them. Making assumptions without taking such hidden factors into account could lead to confusing results.

### 3. What is a directionality problem?

Directionality problem deals with situations when it is not clear which way the dependency is going: does A cause B or rather B causes A?

There are two main directional relationship types:

• unidirectional, with one variable impacting the other
• bidirectional, where both variables impact each other.

Before you find out how actually this influence works, it is early to make any causation assumptions as errors can be introduced at this step.

### 4. What’s the difference between correlational and experimental research?

The difference between correlational and experimental research lies within the area of practical application of results which have been received during it. Correlational research is high in external validity while experimental research is high in internal validity. Each of these approaches is most efficient in different cases, depending on what your purpose is.

Article posted on:Nov 23, 2022
Article updated on:Nov 23, 2022