Probably the most widely known and used of all distributions is the normal distribution. It fits many human characteristics, such as height, weight, speed etc. Many living things in nature, such as trees, animals and insects have many characteristics that are normally distributed. Many variables in business and industry are also normally distributed.

Discovery of the normal curve is generally credited to Karl Gauss (1777 – 1855), who recognized that the errors of repeated measurement of objects are often normally distributed. Sometimes, the normal distribution is also called the Gaussian distribution.

The normal distribution has the following characteristics:

It is a continuous distribution

It is symmetrical about the mean. Each half of the distribution is a mirror image of the other half.

It is asymptotic to the horizontal axis. That is, it does not touch the x-axis and it goes on forever in each direction.

It is unimodal. The normal curve is sometimes called a bell-shaped curve. All the values are “bunched up” in only one portion of the graph – the center of the curve.

It is a family of curves. Every unique value of the mean and every unique value of the standard deviation result in a different normal curve.

The area under the curve is 1. The area under the curve yields the probabilities, so the total of all probabilities for a normal distribution is 1. Since the distribution is symmetric, the area of the distribution on each side of the mean is 0.5.

Probability density function of the normal distribution

The normal distribution is described by two parameters: the mean, , and the standard deviation . We write

The formula for Normal distribution is

Since the formula is so complex, using it to determine area under the curve is cumbersome and time consuming. Instead, tables are used to find the probabilities for the normal distribution. This will be discussed in the lesson on Z-Score.

The following video explores the normal distribution

Presentation on spreadsheet to show that the normal distribution approximates the binomial distribution for a large number of trials.

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