Answer:
Predictive validity.
Step-by-step explanation:
A correlation can be defined as a numerical measure of the relationship existing between two variables (x and y).
In Mathematics and Statistics, a group of data can either be negatively correlated, positively correlated or not correlated at all.
1. For a negative correlation: a set of values in a data increases, when the other set begins to decrease. Here, the correlation coefficient is less than zero (0).
2. For a positive correlation: a set of values in a data increases, when the other set also increases. Here, the correlation coefficient is greater than zero (0).
3. For no or zero correlation: a set of values in a data has no effect on the other set. Here, the correlation coefficient is equal to zero (0).
A predictive validity can be defined as a measure of the extent to which a test score on a scale is able to predict scores based on certain criteria. Thus, it's a measure of the degree to which a certain criteria can predict future behavior or performance.
One of the most common area in which predictive validity is used is in university or college admissions such as in how scores on an assessment or test are related to performance based on a standard or criterion.
In this scenario, there exists a statistical relationship between the results of the assessment conducted and number of students who were successful. Thus, this will best indicate that the assessment has predictive validity.