The Z-score table is a mathematical table for the values of ϕ, indicating the values of the cumulative distribution function of the normal distribution. Z-Score, also known as the standard score, indicates how many standard deviations an entity is, from the mean. Since probability tables cannot be printed for every normal distribution, as there is an infinite variety of normal distribution, it is common practice to convert a normal to a standard normal and then use the z-score table to find probabilities.
Thus, A z-score table shows the percentage of values (usually a decimal figure) to the left of a given z-score on a standard normal distribution.
It is a way to compare the results from a test to a “normal” population.
If X is a random variable from a normal distribution with mean (μ) and standard deviation (σ), its Z-score may be calculated by subtracting mean from X and dividing the whole by standard deviation.
For the average of a sample from a population ‘n’, the mean is μ and the standard deviation is σ.
How to Interpret z-Score
- A z-score of less than 0 represents an element less than the mean.
- A z-score greater than 0 represents an element greater than the mean.
- A z-score equal to 0 represents an element equal to the mean.
- A z-score equal to 1 represents an element, which is 1 standard deviation greater than the mean; a z-score equal to 2 signifies 2 standard deviations greater than the mean; etc.
- A z-score equal to -1 represents an element, which is 1 standard deviation less than the mean; a z-score equal to -2 signifies 2 standard deviations less than the mean; etc.
- If the number of elements in the set is large, about 68% of the elements have a z-score between -1 and 1; about 95% have a z-score between -2 and 2, and about 99% have a z-score between -3 and 3.