What is defined as a statistical measure of the strength and direction of a relationship between two variables?

The measure that indicates both the strength and direction of a relationship between two variables is correlation. Correlation quantifies how closely two variables are related to each other, with values typically ranging from -1 to 1. A correlation of 1 signifies a perfect positive relationship, meaning as one variable increases, the other also increases. Conversely, a correlation of -1 indicates a perfect negative relationship, where one variable increases as the other decreases. A correlation of 0 indicates no relationship between the variables.

This concept is fundamental in statistics because understanding the nature of the association between variables can provide insights into potential patterns and behaviors within the data. Correlation is crucial for various analyses, particularly in psychological research, where understanding relationships between different psychological constructs is essential for interpreting results and drawing conclusions.

In contrast, other terms like variance measure the dispersion of a single variable, while regression predicts the value of one variable based on another. Causation refers to a direct cause-and-effect relationship, which is a stronger claim than what correlation provides and requires further evidence beyond correlation alone.