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 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.
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.

