Summary: Normal distributions are commonly used in Statitics. While normal distributions can be quite different, they can all be represented mathematically and they all have six distinct features that will be discussed in this chapter.
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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 Karl-Friedrich Gauss. As you will see in the section on the history of the normal distribution, although Gauss played an imporant role in its history, de Movire first discorved 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.
| Varieties of Normal Distributions |
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The density of the normal distribution (the height for a given
value on the x axis) of the normal distribution is shown below (Equation 1). The parameters
Since this is a non-mathematical 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.