Before we get into the core of the blog post...
I understand that some of you are looking for a simple, one-sentence solution. So, if you're looking for a quick explanation of causation vs correlation, here it is:
Correlation is a relationship between two variables in which when one changes, the other changes as well.
Causation occurs if there is a real justification for why something is happening logically. It suggests that there is a cause-and-effect relationship.
As a result, causality is a correlation with a cause.
Continue reading if you want to learn more about these terms, how to differentiate them, and how to apply what you've learned to real-life situations.
What is the definition of correlation vs causation?
The degree of relationship between two random variables is referred to as correlation in statistics. As a result, the correlation between two data sets is the degree to which they are similar.
You're pointing out a correlation between A and B if A and B are frequently noticed simultaneously. You're not arguing that A causes B or that B causes A. Simply put, you're stating that when A is observed, B is also noticed. They move in lockstep or appear at the same time.
We can distinguish between three sorts of correlations:
- A positive correlation occurs when A rises and B rise at the same time. Alternatively, if A falls, B falls as well. For instance, the more purchases made in your app, the more time people spend using it.
- A negative correlation appears when A rises, B falls, or vice versa.
- When two variables are not connected, and a change in A causes no changes in B, or vice versa, there is no correlation.
Causation implies that A and B are linked in a cause-and-effect connection. So you're telling that A causes B.
The term "causation" is also used to refer to causality.
- To begin with, causality denotes the occurrence of two occurrences simultaneously or one after the other.
- Secondly, it indicates that not only do these two factors emerge together, but their presence also causes the other to manifest.
What is the distinction between causation and correlation?
While causation and correlation can coexist, correlation does not necessarily imply causation. Causation refers to situations in which action A causes outcome B. Correlation, on the other hand, is merely a relationship. Action A is related to Action B, but one event may not always lead to the occurrence of the other.
Correlation and causation are frequently misconstrued because the human mind wants to detect patterns even when they don't exist. We typically construct these patterns when two variables appear to be so closely related that one is dependent on the other. That implies a cause-and-effect relationship, with the dependent event being the result of an independent event.
Even if two occurrences appear to be coinciding before our eyes, we cannot simply conclude causation. For starters, our observations are entirely based on anecdotal evidence. Two, there are a plethora of additional options for forming a partnership, including:
- The contrary is true: B causes A.
- The two are correlated, but there's more to it: A and B are associated, but C is the cause.
- There's another factor at play: A does trigger B—if D occurs.
- There's a chain reaction going on: A causes E and E to produce B.
Correlation and Causation Difference with Examples
Knowing the distinction between correlation and causation is critical, especially when deciding based on potentially incorrect information.
Such, how do you test your data so that you can make certain causation claims? There are five approaches to this, referred to as the design of experiments in technical terms. We've ranked them from most reliable to least reliable:
1. A Randomized Controlled Trial
Setting up a randomized experiment is the best technique to prove causation. This is where you assign people to the experimental group at random.
There is a control group and an experimental group in experimental design, both with equal conditions but one independent variable being examined. By assigning persons to the experimental group at random, you eliminate experimental bias when one outcome is favored over another.
For example, you'd assign customers to test the new shopping cart you've prototyped in your app at random, while the control group would utilize the present (old) shopping cart.
2. Study of a Quasi-Experimental Nature
But what if you can't randomize the procedure of picking participants for the study? This is a design that is akin to an experiment. There are six different kinds of quasi-experimental designs, each with its own set of uses.
To obtain the necessary knowledge, quasi-experimental investigations will often require more complex statistical approaches. In addition, surveys, interviews, and observational notes may all be used by researchers, further complicating the data processing process.
For example, you're evaluating whether the latest iteration of your app's user interface is less confusing than the previous one. And you're using a select group of beta testers for your software. Unfortunately, as they all raised their hands to acquire access to the latest enhancements, the beta test group was not random. As a result, showing correlation vs. causation – or, in this case, UX confusing – is more complicated than proving causality with random experimental research.
3. A Correlational Analysis
When attempting to discover if two variables are connected or not, a correlational analysis is used. A correlation exists when A rises and B rises at the same time. You'll be fine if you remember that correlation does not indicate causation.
For example, suppose you want to see if a smoother user experience correlates with higher app store ratings. And after some observation, you'll notice that when one grows, the other grows as well. So, you're not claiming that A (smooth UX) causes B (higher ratings); instead, you're claiming that A is highly linked to B. And, you might even be able to foresee it. So, there's a link there.
4. Research on a single subject
Because it focuses on a single subject, single-subject design is more commonly utilized in psychology and education. Instead of having control and an experimental group, the subject is the control. Attempting to modify the individual's behavior or thinking concerns the researcher.
For example, single-subject research in mobile marketing might entail asking one unique user to test the usability of a new app feature. You can have them repeat an activity on the current app numerous times before having them try the identical action on the new app version. Then, collect data to check if the old or new app operates faster.
On a concluding note,
We are constantly looking for patterns, so our default goal is to explain what we find. However, unless causation can be established, it's safe to presume we're merely witnessing correlation.
Events that appear to be connected based on common sense cannot be considered causative unless a clear and direct link can be shown. And, while causation and correlation can coexist, correlation does not always imply causation.
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