Answer & Explanation:
First, correlation and causation both need an independent and dependent variable. An independent variable is a condition or piece of data in an experiment that can be controlled or changed. A dependent variable is a condition or piece of data in an experiment that is controlled or influenced by an outside factor, most often the independent variable.
If there is a correlation, then sometimes we can assume that the dependent variable changes solely because the independent variables change. This is where the debate between correlation and causation occurs. However, there is a difference between cause and effect (causation) and relationship (correlation). Sometimes these areas can be confused and muddled when analyzing data.
- You probably know that a correlation is the relationship between two sets of variables used to describe or predict information. There is an emphasis here on relationship. Sometimes we can use correlation to find causality, but not always. Remember that correlation can either be positive or negative.
A positive correlation, where the dependent variables and independent variables in a data set increase or decrease together. If the numbers sloped downward, then you have a data set with a negative correlation, where the dependent variables and independent variables in a data set either increase or decrease opposite from one another.
Whilst a negative correlation means if the independent variables decrease, then the dependent variable would increase, and vice versa.
- Causation, also known as cause and effect, is when an observed event or action appears to have caused a second event or action. For example, I bought a brand new bed comforter and placed it in my washing machine to be cleaned. After cleaning the comforter, my washing machine stopped working. I may assume that the first action, washing the comforter, caused the second action, broken washing machine.
<em>I hope this helped! :)</em>
