Z-score standardization is a statistical technique used to transform data into a common scale. It calculates how many standard deviations a data point is from the mean of the dataset. The formula for the z-score is: z = \frac(X - \mu)\sigma , where X is the value, \mu is the mean, and \sigma is the standard deviation. This process helps in comparing scores from different datasets. By converting values into z-scores, researchers can easily identify outliers and understand the relative position of data points. A z-score of 0 indicates that the value is equal to the mean, while positive or negative scores show how far and in which direction the value deviates from the mean.