![]() ![]() This process of converting a raw score into a standard score is called standardizing or normalizing (however, "normalizing" can refer to many types of ratios see Normalization for more). It is calculated by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. Raw scores above the mean have positive standard scores, while those below the mean have negative standard scores. In statistics, the standard score is the number of standard deviations by which the value of a raw score (i.e., an observed value or data point) is above or below the mean value of what is being observed or measured. Comparison of the various grading methods in a normal distribution, including: standard deviations, cumulative percentages, percentile equivalents, z-scores, T-scores For Z-score financial analysis tool, see Altman Z-score. For Z-factor in high-throughput screening, see Z-factor. For z-transformation to complex number domain, see Z-transform. ![]() For Fisher z-transformation in statistics, see Fisher transformation. ![]()
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