Final answer:
The term that refers to the degree to which scores in one distribution explain scores in another distribution is the correlation coefficient, option A. This coefficient, denoted as r, can range from -1 to +1, signaling the strength and direction of the relationship between the variables.
Step-by-step explanation:
The degree to which scores in one distribution explain scores in another distribution is referred to as the correlation coefficient. This is essentially a measure of how strongly two variables are related to each other. For instance, the correlation coefficient, represented as r, can range from -1 to +1, indicating both the strength and the direction of the relationship between variables. A correlation coefficient of -1 signifies a perfect negative correlation, meaning as variable x increases, variable y decreases. Conversely, a coefficient of 1 indicates a perfect positive correlation, where variables x and y both increase together. A coefficient of 0 suggests no correlation, meaning that the variables do not appear to influence each other at all.
The value of r reveals the extent of the linear relationship between the two variables. In addition, the coefficient of determination, or r², informs us about the percentage of variance in the dependent variable that can be accounted for by the independent variable. Notably, one should be cautious not to interpret a correlation as implying causation; a strong correlation does not confirm that one variable causes changes in another, only that they are associated.
The correct option for the degree to which scores in one distribution explain scores in another distribution is A) Correlation Coefficient, hence, the correlation coefficient is the statistical tool that measures the extent to which two variables are correlated.