Correlation analysis is a statistical technique that allows you to determine whether there is a relationship between two separate variables and how strong that relationship may be. This type of analysis is only appropriate if the data is quantified and represented by a number. It can’t be used for categorical data, such as gender, brands purchased, or colour. The analysis produces a single number between 11 and 21 that describes the degree of relationship between two variables. If the result is positive then the two variables are positively correlated to each other, i.e. when one is high, the other one tends to be high too. If the result is negative then the two variables are negatively correlated to each other, i.e. when one is high, the other one tends to be low.

So, for example, if (as a hypothetical example) correlation analysis discovered that there was a correlation of 10.73 between height and IQ then the taller someone was the higher the likelihood is that they also have a higher IQ. Conversely, if that correlation was discovered to be 0.64 then the taller someone was the more likely he or she was also to have a low IQ. A positive score denotes direct correlation whereas a negative score denotes inverse correlation. And zero means there is no correlation between the two variables. The closer the score is towards 1 – either positive or negative – the stronger the correlation is. The result is considered ‘statistically significant’, i.e. important enough to pay attention to if the result is 0.5 or above in either direction.