A commonly used transformation of a normal distribution.
It is used extensively in hypothesis testing.
The Z statistic is also known as the 'Z score'.
So - for example - if a data point has a Z score (or Z statistic) of -1.64, then it lies 1.64 standard deviations below the mean.
The Z score is calculated as the difference between the data point (X) and the mean E[x], all divided by the standard deviation of the population (SD).
For example if:
the mean (E[x]) of a population = 100;
the standard deviation (SD) = 10; and
a given observation (or data point) = 83.6;
then the Z score (Z) is calculated as: Z = (X - E[x])/SD
= (83.6 - 100 = -16.4)/10
= - 1.64 standard deviations.
In this case the Z score is negative, indicating that the data point (83.6) lies below the mean (of 100).