Introduction to Normal Distributions
Prerequisites
Distributions,
Central Tendency, Variability
Learning Objectives
 Describe of shape of normal distribution
 State 7 features of normal distributions
The normal distribution is the most important
and most widely used distribution in statistics. It is sometimes
called the "bell curve" although the tonal qualities
of such a bell would be less than pleasing. It is also called
the "Gaussian
curve" after the mathematician KarlFriedrich Gauss. As
you will see in the section on the history
of the normal distribution,
although Gauss played an important role in its history, de Movire
first discovered the normal distribution.
Strictly speaking, it is not correct to talk about
"the normal distribution"
since there are many normal distributions. Normal distributions
can differ in their means and in their standard deviations. Figure
1 shows two normal distributions. The blue distribution has a
mean of 50 and a standard deviation of 10; the distribution in
red has a mean of 60 and a standard deviation of 5. Both distributions
are symmetric with relatively more values at the center of the
distribution and relatively few in the tails.
The density of the normal distribution (the height
for a given value on the x axis) of the normal distribution is
shown below. The parameters μ and
σ are the mean and standard deviation
respectively and define the normal distribution. The symbol e
is the base of natural logarithm and π
is the constant pi.
Since this is a nonmathematical treatment of statistics, do not
worry if this expression confuses you. We will not
be referring back to it in later sections.
Six features of normal distributions are listed
below. These features are illustrated in more detail in the remaining
sections of this chapter.
 Normal distributions are symmetric around their mean.
 The mean, median, and mode of a normal distribution
are equal.
 The area under the normal curve is equal to 1.0.
 Normal distributions are denser in the center and less
dense in the tails.
 Normal distributions are defined by two parameters,
the mean (μ) and the standard
deviation (σ).
 68% of the area of a normal distribution is within
one standard deviation of the mean.
 Approximately 95% of the area of a normal distribution
is within two standard deviations of the mean.

