DISCOVER

Updated March 23, 2017

Researchers that collect quantitative, or numerically based, data perform various forms of statistical analysis to draw conclusions from this data. By looking for relationships between different data sets they collect, researchers can test hypotheses about how different factors affect one another, and how strong these effects are. One such method, testing for statistical correlations, has limited academic value.

## Correlations

Correlations are a simple form of statistical analysis that looks for numerical relationships between two equally sized data sets. By comparing numbers from two different data sets together, correlations look at how movement in the value of numbers in one data set is related to movement in the value of numbers in the other data set. For example, a researcher could look at correlations between the hours students spend studying and their test scores to see if there is a relationship between hours spent studying and test scores. The equation to test for correlations reports this relationship as a coefficient that is between zero (absolutely no relationship between the two data sets) and one (a perfect relationship between the two data sets) that is either positive (an increase in one data set is related to an increase in the other data set) or negative (an increase in one data set is related to a decrease in the other data set).