# Data Science - Statistics Correlation vs. Causality

## Correlation Does Not Imply Causality

Correlation measures the numerical relationship between two variables.

A high correlation coefficient (close to 1), does not mean that we can for sure conclude an actual relationship between two variables.

A classic example:

- During the summer, the sale of ice cream at a beach increases
- Simultaneously, drowning accidents also increase as well

Does this mean that increase of ice cream sale is a direct cause of increased drowning accidents?

## The Beach Example in Python

Here, we constructed a fictional data set for you to try:

### Example

```
import pandas as pd
```

import matplotlib.pyplot as plt

Drowning_Accident = [20,40,60,80,100,120,140,160,180,200]

Ice_Cream_Sale =
[20,40,60,80,100,120,140,160,180,200]

Drowning = {"Drowning_Accident":
[20,40,60,80,100,120,140,160,180,200],

"Ice_Cream_Sale":
[20,40,60,80,100,120,140,160,180,200]}

Drowning = pd.DataFrame(data=Drowning)

Drowning.plot(x="Ice_Cream_Sale", y="Drowning_Accident", kind="scatter")

plt.show()

correlation_beach = Drowning.corr()

print(correlation_beach)

Try it Yourself »
Output:

## Correlation vs Causality - The Beach Example

In other words: can we use ice cream sale to predict drowning accidents?

The answer is - Probably not.

It is likely that these two variables are accidentally correlating with each other.

What causes drowning then?

- Unskilled swimmers
- Waves
- Cramp
- Seizure disorders
- Lack of supervision
- Alcohol (mis)use
- etc.

Let us reverse the argument:

Does a low correlation coefficient (close to zero) mean that change in x does not affect y?

Back to the question:

- Can we conclude that Average_Pulse does not affect Calorie_Burnage because of a low correlation coefficient?

The answer is no.

There is an important difference between correlation and causality:

- Correlation is a number that measures how closely the data are related
- Causality is the conclusion that x causes y.

It is therefore important to critically reflect over the concept of causality when we do predictions!