The Difference between Correlation and Causation survey collection tools

The Difference between Correlation and Causation


Table of Contents


Correlation and causation are two related but distinct concepts. It is vital to understand the differences between them in order to effectively evaluate and interpret scientific research. Within this article, we will explore correlation and causation separately before delving into their comparisons.

Transform your insight generation process

Create an actionable feedback collection process.

online survey


In analytics, correlation is a term that is used to indicate a statistical association between variables; when one variable changes, so does the other. However, it is important to note that this covariation doesn’t necessarily imply a direct or indirect causal link between the variables. 

There are three main types of correlations between variables: 

  1. Positive Correlation: When variables have a positive correlation, it means they move in the same direction. When x increases, y increases and when x decreases, y decreases. 
  2. Negative Correlation: When variables have a negative correlation, it means they move in opposite directions. When x increases, y decreases and when x decreases, y increases. 
  3. No Correlation: When variables have no correlation, it means there is no relationship between them. When x increases, y either stays the same or has no distinct pattern. 

Consider the following example: When the temperature rises in the summer, ice cream sales and swimsuit sales both see an increase. However, although ice cream sales and swimsuit sales do depict a positive correlation, there isn’t a causal link between these variables. Rather, the rise in temperature separately influences both these variables resulting in their positive correlation. 


In analytics, causation is a term that is used to indicate a causal relationship between two variables; one variable is dependent on the other. In a causal relationship, there is an independent variable (‘the cause’) and a dependent variable (‘the effect’). 

Let’s continue with the aforementioned example:

We’ve established that ice cream sales and swimsuit sales depict a positive correlation in the summer due to the rise in temperature. In this example, temperature has a causal relationship with both ice cream and swimsuit sales separately. When the temperature rises, more people purchase ice creams; when the temperature rises, more people purchase swimsuits.

The Difference between Correlation and Causation

Causation always implies correlation, but correlation does not imply causation. But why?

Correlation simply implies a relationship between variables, but does not indicate that the covariation exists due to a direct or causal link between them. Conversely, causation goes beyond implying a relationship; it implies a specific type of relationship known as a causal relationship (or cause-and-effect relationship). For a causal relationship to exist between two variables, one must influence the other; when one variable changes, it must bring about a change in the other. The variables, therefore, have a causal link. 

Reflecting back on the initial statement; variables that have a causal relationship are related and, therefore, causation always implies correlation. However, as variables can be related without directly influencing each other, correlation does not imply causation.

Download Market Research Toolkit

Get market research trends guide, Online Surveys guide, Agile Market Research Guide & 5 Market research Template

Making the most of your B2B market research in 2021 PDF 3 s 1.png

Correlation does not Imply Causation

Let’s understand a few reasons as to why we cannot imply causation even if we identify correlation:

  • Third/Confounding Variable Issue: Sometimes, the two variables being studied are related due to a third variable. In such cases, the third variable influences both the other variables separately, indicating that the variables being studied do not have a cause-and-effect relationship.
  • Directionality Issues: In certain cases where variables do have a causal relationship, there are directionality issues. This means that it is hard to decipher which variable is ‘the cause’ (also known as the independent variable) and which is ‘the effect’ (also known as the dependent variable). In such situations, where the causal relationship cannot be proven, we cannot imply causation. 
  • Chain Reaction: There may be multiple different variables influencing the correlation between the two variables being studied. This chain reaction indicates that the variables do not have a cause-and-effect relationship but are simply correlated to one another. 

See Voxco survey software in action with a Free demo.

FAQs on Correlation and Causation

A few reasons why correlation may not imply causation are;

  • Directionality issues make it hard to pinpoint which variable is independent and which is dependent. 
  • A chain reaction involving multiple other extraneous variables is resulting in the correlation between the variables being studied.

Correlation can be defined as a statistical association between variables.

Causation implies that a change in one variable influences a change in the other and there is, therefore, a causal link between them.

Correlation simply implies a statistical association, or relationship, between two variables. Causation, on the other hand, not only implies a relationship, it implies a causal relationship; it implies that a change in one variable is directly causing a change in the other.

Explore all the survey question types
possible on Voxco

Read more

Daily Active Users

Daily Active Users SHARE THE ARTICLE ON Share on facebook Share on twitter Share on linkedin Table of Contents When we’re in the subscription company,

Read More »