Normal distribution has a complex probability density function.

The normal distribution that appears on table has an average of 0 and a standard deviation of 1.

Z is the value of a point on the on this distribution also represented by N(0,1)


How to use the normal distribution table

To use the table it must have one of these questions:

  • What is the acumulated value of probability for a given Z
  • What is the Z that has an certain acumulated probability.

The 2 are related, on the table below Z value is 1,13 and the acumulated probability for this value is 0,87076.

On the left columns it is represented the decimal value of Z and on the first line the centesimal part.

normal distribution table

The value of 0,87076 is the value of the blue area. The area below the black line is allways 1.

Normal distribution table

[table id=1 /]


Usually normal distribution table just shows the value of positive Z.

The normal distribution is simetric and o 0 it has 50% of probability below 0 and 50% above 0.


The values from the table are represented as \Phi(z).

The simetry rule makes that negative values of Z can be obtained with the formula:



Values of Z below -4 are considered to have value 0 and values above 4 are considered to be 1.

Normal distribution in programs

In Excel the normal distribution has the function

NORMDIST(x, mean, standard_dev, cumulative)


  • x — The value you want to test.
  • mean — The average value of the distribution
  • standard_dev — The standard deviation of the distribution.
  • cumulative — If FALSE or zero, returns the probability that x will occur; if TRUE or non-zero, returns the probability that the value will be less than or equal to x.

And to get the value of Z that relates to a probability there is the function

NORMINV(probability, mean, standard_dev)


In R with Rcmdr is possible to draw the distribution.

History of normal distribution

“Independently, the mathematicians Adrian in 1808 and Gauss in 1809 developed the formula for the normal distribution and showed that errors were fit well by this distribution.

This same distribution had been discovered by Laplace in 1778 when he derived the extremely important central limit theorem, (…). Laplace showed that even if a distribution is not normally distributed, the means of repeated samples from the distribution would be very nearly normally distributed, and that the larger the sample size, the closer the distribution of means would be to a normal distribution.”