Why might z-scores be used less frequently than other scoring methods?

Z-scores represent the number of standard deviations a data point is from the mean of its dataset. While they provide a standardized way of understanding relative performance and allow for comparison across different datasets, interpreting z-scores can be less intuitive compared to other scoring methods such as percentiles or raw scores. This is because z-scores can yield negative or fractional values, which may not be immediately comprehensible to individuals who are more accustomed to seeing scores that are whole numbers and range from 0 to 100 or percentages.

In many educational and psychological assessments, simpler scoring methods are preferred because they offer a more straightforward interpretation. For example, percentiles indicate the percentage of scores that fall below a particular score, which is more easily understood than the concept of standardized units represented by z-scores. Consequently, this can make z-scores less practical for everyday use, especially in contexts where clear communication of results is important. Therefore, while z-scores have their advantages in terms of statistical analysis, their interpretative complexity can limit their frequent application compared to more intuitive scoring methods.