The Zscore represents the distance between a raw score (x) and the population mean in units of the standard deviation (s): z is negative when the raw score is below the mean, positive when above.
The ZScore is used by developing the idea of the Standard Deviation a little further and to assess the probability of any occurence over the stated range of values. It is therefore used to predict the probability of an event (see example 1) or of a range of events (see example 2), based on actual observations.
If you have two sets of data which are at the ordinal or interval level AND both sets are normally distributed AND there is a linear correlation, you can also use a Linear Regression analysis to predict values outside of the sample.
The ZScore is used by developing the idea of the Standard Deviation a little further and to assess the probability of any occurence over the stated range of values. It is therefore used to predict the probability of an event (see example 1) or of a range of events (see example 2), based on actual observations.
If you have two sets of data which are at the ordinal or interval level AND both sets are normally distributed AND there is a linear correlation, you can also use a Linear Regression analysis to predict values outside of the sample.
Example 1: what is the probability of a completely dry month of February in San Francisco?
Rainfall (in cm) in the past 10 years in February in SF was as follows:

Example 2: what is the probability to have a range between 6 and 8 cm of rain in the next month of February?
Stated values (based on the values collected in Example 1):
The probability that the next month of February has between 6 and 8 cm of rain is 30.5%, based on the data collected in the past ten years. 