When applying to graduate school, the admissions committee compares the candidate's scores on the GRE with the candidate's grade point average reported on the college transcripts. This is an example of:

Please provide the options or the context for what this is an example of, such as "standardized testing" or "academic evaluation."

When graduate school admission committees compare a candidate's GRE scores with their GPA, they are using bivariate data analysis in statistics. This method helps determine if there's a relationship between the two variables and provides a broader understanding of a student's overall academic performance.

Explanation:

The process of comparing a candidate's scores on the GRE with their grade point average (GPA) reported on the college transcripts, as done in graduate school admissions, is an example of bivariate data analysis in statistics. Bivariate analysis examines two variables (in this case, GRE scores and GPA) to determine if there is a relationship between them. The goal is to compare the performance measures (test scores and GPA) to predict success in graduate school.

Statistical methods are widely used to make such comparisons and analyses in graduate admissions. For example, a student with a high GPA but low GRE scores might be compared to a student with average scores on both measurements. This kind of data analysis helps to create a more comprehensive understanding of a student's academic prowess beyond simply relying on test scores. However, it's important to note that there are limitations to these methods as they don't fully capture a student's ability, potential, or the diversity of their experiences and skills.

Learn more about Bivariate Data Analysis here:

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