What represents standard scores in testing?

Standard scores in testing are a way of interpreting test results by placing them in a statistical context, specifically through normalization. When scores are converted to a bell-shaped curve, known as a normal distribution, they are aligned with a predetermined mean and standard deviation. This conversion allows for a clearer understanding of how an individual score compares to a broader population.

Using standard scores, like Z-scores, provides a standard method for comparing scores across different tests or different groups, regardless of the original scoring system. For instance, a student’s performance can be evaluated not just on the score they achieved, but also in terms of how that score stands in relation to the average score and the spread of scores among all test-takers. This makes it easier to assess relative performance and identify how typical or atypical a specific score is in a larger context.

The other options lack this statistical framework necessary for understanding standard scores. Raw scores alone do not provide meaningful context without comparison to a mean or variability among scores. Scores focused on only the highest achievers do not reflect a complete picture of performance and do not help in comparing all test-takers equally. Scores that are not influenced by class performance do not align with standardization, which inherently considers a population of scores to establish norms