Quick Correlation and Causality: A Scientist's Perspective

In summary, a correlation coefficient that is very near to zero cannot be used to indicate that there is no causality between two variables. This is because causality does not always imply correlation, as the relationship between variables can be nonlinear. Therefore, a zero correlation does not necessarily mean there is no causality, unless a non-linear relationship can be ruled out.
  • #1
clane
1
0
Hello All,

I am trying to look at some correlation numbers and while I am aware that correlation does not always indicate causality can a correlation coeffiecent that is very near to zero be used to indicate that it is not a cause?

Thanks

C
 
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  • #2
Elementary logic should give you the answer.

Causality => correlation.

Therefore: not correlation => not causality.
 
  • #3
Let's be careful.

Causality does not always imply correlation because the relationship may be nonlinear. Correlation is a linear relationship.

If two variables are dependent, their relationship can be linear or nonlinear. If it's linear, their correlation will be significant. If their corr. is not significant, then you can say that the variables are not LINEARLY dependent on each other. But, they can still be non-linearly dependent.

So the answer is technically "No, a zero correlation does not mean no causality." A more roundabout way of putting it is: "A zero correlation would imply no causality only if a non-linear relationship can be excluded on other grounds."
 
Last edited:

1. What is a quick correlation problem?

A quick correlation problem is a statistical analysis technique used to measure the strength and direction of the relationship between two variables. It is a way to determine if there is a pattern or trend between the two variables.

2. How is a quick correlation problem calculated?

A quick correlation problem is calculated using a statistical measure called the correlation coefficient, which ranges from -1 to +1. A positive correlation coefficient indicates a positive relationship, while a negative correlation coefficient indicates a negative relationship. The closer the correlation coefficient is to 1 or -1, the stronger the relationship between the variables.

3. What are some examples of quick correlation problems?

Some examples of quick correlation problems include studying the relationship between exercise and weight loss, or the relationship between hours studied and test scores. It can also be used in market research to analyze the relationship between price and consumer demand for a product.

4. What are the limitations of a quick correlation problem?

One limitation of a quick correlation problem is that it only measures the linear relationship between two variables, which means it may miss more complex relationships. Additionally, correlation does not imply causation, so a strong correlation does not necessarily mean that one variable causes the other.

5. How can a quick correlation problem be used in research?

A quick correlation problem can be used in research to explore potential relationships between variables and to identify patterns or trends. It can also be used to test hypotheses and make predictions. However, it should always be used in conjunction with other statistical analysis techniques to ensure accurate and reliable results.

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