# Correlation causal relationship difference

### Correlation vs Causation: Definition, Differences, & Examples | CleverTap

What is the difference between correlation and causation? a tool that gives insight into the relationships between factors in a given analysis. This animation explains the concept of correlation and causation. Theoretically, the difference between the two types of relationships are. While causation and correlation can exist at the same time, correlation A correlation is a relationship that you observe between two variables.

You'll not ace this test because it's sunny. You'll ace test because you studied. The fact that it's sunny outside has nothing to do with it. I always ace my tests when it's sunny. The sunniness does not cause you to ace the test. Andy's making a mistake right?

How Ice Cream Kills! Correlation vs. Causation

The sunniness doesn't cause Andy to ace his test. The two events, the sunniness and Andy acing his test occur together without one causing the other.

In other words, the two events are correlated in some way but there's no causal relation between them. Andy's reasoning here is fallacious. Simply because two events are correlated does not mean that one caused the other. This conflation of correlation and causation is what we will talk about in this video.

## What’s the difference between Causality and Correlation?

First let's consider some other examples. Fido barks when his tail wags. People with higher grades in college have higher grades in high school. People who take vitamin C recover more quickly from a cold. That is, there's a correspondence between these events. For example, the dog, Fido, barks when his tail wags but there's no reason to suspect that there's a causal relation between these events. While these events often occur together There are many times when Fido's tail wags and he doesn't bark and there are times when Fido barks but doesn't wag his tail.

Furthermore, we may suspect that there is some common cause for these events like Fido's excitement when his owner comes home.

Now that we can agree that these are cases of correlation without causation We can discuss two types of correlation, positive and negative. In the next video we'll discuss how these types of correlations specifically relate to different types of causation. But for now let's just introduce them. When events frequently occur together like in the examples above they are positively correlated. If two events are positively correlated Then when one event is present the others often present as well.

In our first example it being a sunny day in Arizona is positively correlated with Andy succeeding on his math test. On the other hand, two states are negatively correlated when it's likely that when one event occurs the other will not occur. For instance, when it snows, it's often not very sunny, so snowing and sunniness are negatively correlated. After all, bad smells and disease are both unpleasant, and always seem to appear at the same time and in the same places.

But you can have a foul odor without a disease. To prove causation, you need to find a direct relationship between variables. You need to show that one relies on the other, not just that the two appear to move in concert.

When it comes to your business, it is imperative that you make the distinction between what actions are related and what caused them to happen. How correlation gets mistaken for causation Picture this: Thirty days into the new app being out, you check your retention numbers. Users who joined at least one community are being retained at a rate far greater than the average user. This seems like a massive coup.

All you know is that the two are correlated. You have no idea what other factors are at play, what other behaviors those users took part in besides joining a community.

### Difference Between Causality And Correlation? | Business Analytics Tool

Once you lay out the variables, you can control and change them to meet your needs. Look at each of your variables, change one and see what happens. Do something that prioritizes the input variable and increases it, possibly at the expense of something else.