Which score can be transformed to fit a normal curve?

A standardized score is specifically designed to be transformed so that its distribution fits a normal curve. This transformation involves adjusting the raw data to account for the mean and standard deviation of the data set, resulting in a score that can be compared across different distributions. Standardized scores typically follow a bell-shaped curve, adhering to the properties of the normal distribution.

In contrast, raw data is the original form of data collected from a sample, which may not have a normal distribution. It requires further processing or transformation before any conclusions can be drawn regarding its fit to a normal curve. Qualitative scores are categorical and cannot be transformed into a numerical format suitable for distribution fitting. Descriptive statistics provide a summary of the data but do not themselves represent scores that fit a normal distribution. Thus, among the given choices, standardized scores are the only type that can be effectively transformed to align with the characteristics of a normal distribution.