What is Correlation and Causation, What is so Important?

When it comes to data analysis, understanding the relationship between variables is crucial. Two key concepts that often arise in this context are correlation and causation. They might sound similar, but they have distinct meanings that can significantly impact our understanding of data. In this blog post, we’ll journey into the world of correlation and causation, exploring their differences, implications, and real-world applications.

Table of Contents

Correlation

Correlation is like observing a dance between variables. When two variables move in a similar pattern, we say they have a correlation. But remember, correlation doesn’t mean one variable causes the other – it’s like two friends showing up to a party in similar outfits. There are different levels of correlation: positive when one variable goes up as the other goes up, negative when one goes up as the other goes down, and no correlation when there’s no clear pattern. To measure correlation, we often use the Pearson correlation coefficient, which ranges from -1 to 1. A correlation coefficient between -1 to 0 is known as a negative correlation and 0 to 1 is known as a positive correlation.

Here we have two examples, positive correlation (left) and negative correlation (right).

Positive Correlation: In this scatter plot, you can see that as one thing goes up, the other thing also goes up. Imagine you’re measuring the amount of sunshine and the growth of plants. When there’s more sunshine, the plants tend to grow taller. The dots on the graph move up and to the right, showing a friendly connection between the two things. This means that when one thing increases, the other thing usually increases too.

Negative Correlation: Now look at this scatter plot where one thing goes up and the other goes down. It’s like a teeter-totter – when one side is up, the other side is down. Think of it like studying and sleep time. When you study more, you might sleep less. The dots on the graph move down and to the right, showing that when one thing increases, the other thing often decreases. It’s like they’re doing a little dance in opposite directions.

When to Use Correlation

Let’s dive into some real-world examples to solidify our understanding. Imagine you’re a data analyst studying a dataset of student performance. You notice a positive correlation between the number of hours students study and their exam scores. However, you can’t automatically conclude that studying longer causes higher scores. It could be that students who are naturally motivated tend to study more and also perform better.

Sample Python Code

Now, let’s talk Python! How can we calculate correlation in Python? Here’s a simple code snippet:

import numpy as np 
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

hours_studied = np.array([4, 6, 3, 7, 5])
exam_scores = np.array([75, 85, 60, 90, 70])

data = {
    'hours_studied': hours_studied,
    'exam_scores': exam_scores
}

sample_data = pd.DataFrame(data)
corr = sample_data.corr()

sns.heatmap(corr, annot=True)
plt.show()

This is how you interpret the result from Python. The following example shows there is a 0.9 correlation score between X₁ and Y₃.

Remember, this is just a simple example, you could import your own dataset and try to see the correlation between variables.

Causation

Causation, on the other hand, is about identifying a cause-and-effect relationship. If one variable directly influences another, we have causation. Imagine flipping a light switch and the room becoming brighter – that’s a simple cause-and-effect scenario. However, determining causation is tricky in real life, as other factors could be at play.

It means that even two highly correlated variables may have no relation with each other. For example, we collected and analyzed data about the beach, and discovered that the ice cream sales increase so did the number of drowning incidents. But clearly, those two variables are not related. Meaning, we could have a possibility of using wrong sets of data when analyzing a problem.

Another example, imagine you’re at a beach enjoying the sunset. You notice that as the sun sets, the temperature starts to cool down. While it might seem like the sun’s setting is causing the temperature drop, there’s more to understand. In reality, the time of day is the real driver here. As the day progresses, it naturally gets cooler, and that’s why the sun is setting and the temperature is dropping at the same time.

So, although there’s a correlation between the sun setting and the temperature cooling, the true cause is the passage of time. This example showcases the importance of digging deeper to uncover the actual factors that influence a situation, rather than assuming a direct cause-and-effect relationship based solely on correlation.

Conclusion

In the world of data analysis, understanding the difference between correlation and causation is crucial. Correlation helps us identify relationships, while causation dives deeper into cause and effect. While they might dance together at times, knowing when to infer one over the other is the key to unravelling insights hidden within the data.

So, as you journey through data, always ask: Are these variables dancing together, or is one truly leading the other? The answers could lead to discoveries that shape decisions and innovations across various fields.

When it comes to data analysis, understanding the relationship between variables is crucial. Two key concepts that often arise in this context are correlation and causation. They might sound similar, but they have distinct meanings that can greatly impact our understanding of data.