Correlation is ​a statistical measure ranging from +1.0 to -1.0, represented by 'r', that indicates how strongly two or more variables are related and whether that relationship is positive or negative.

A positive correlation (+1.0 to 0) indicates that the two variables move in the same direction, e.g., the variable of high school exam results might have a positive correlation with the variable of university exam results. A positive correlation of 0.97 is shown below:

Scatterplot showing positively correlating data points from bottom left corner of chart to top right with a line of best fit

Graphic created with Interpreting Correlations tool (Magnusson, n.d.).


A negative correlation (0 to -1.0) indicates that the variables move in opposite directions, e.g., years spent driving might have a negative correlation with the amount of driving accidents. A negative correlation of -0.97 is shown below:

Scatterplot showing negatively correlating data points from top left corner of chart to bottom right with a line of best fit

Graphic created with Interpreting Correlations tool (Magnusson, n.d.).


The strength of a correlation (how closely the two variables are linked) is indicated by the value of 'r'. If 'r' is +1 or -1, very is a very strong correlation. If 'r' is 0, there is no correlation:

Scatterplot showing data distributed broadly over the chart demonstrating zero correlation between the variables

Graphic created with Interpreting Correlations tool (Magnusson, n.d.).

It's important to note that correlation does not equal causation. While an example of a positive correlation between high school and university exam results above might suggest causation, there is a range of factors that need to be addressed. For example, hours spent studying, family background, life goals, amount of time spent socializing, or intellect might all play a part in causing the correlation. 

Correlation can show the strength and direction of a relationship between variables. But it's up to controlled experiments and well-designed research studies to show causation.


Magnusson, K. (n.d.). 'Interpreting Correlations: an interactive visualization' [webpage]. Retrieved from

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